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1
Ye, Xin. "Automated Software Defect Localization." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1462374079.
Повний текст джерела
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Jain, Achin. "Software defect content estimation: A Bayesian approach." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/26932.
Повний текст джерелаАнотація:
Software inspection is a method to detect errors in software artefacts early in the development cycle. At the end of the inspection process the inspectors need to make a decision whether the inspected artefact is of sufficient quality or not. Several methods have been proposed to assist in making this decision like capture recapture methods and Bayesian approach. In this study these methods have been analyzed and compared and a new Bayesian approach for software inspection is proposed.
All of the estimation models rely on an underlying assumption that the inspectors are independent. However, this assumption of independence is not necessarily true in practical sense, as most of the inspection teams interact with each other and share their findings. We, therefore, studied a new Bayesian model where the inspectors share their findings, for defect estimate and compared it with the Bayesian model (Gupta et al. 2003), where inspectors examine the artefact independently. The simulations were carried out under realistic software conditions with a small number of difficult defects and a few inspectors. The models were evaluated on the basis of decision accuracy and median relative error and our results suggest that the dependent inspector assumption improves the decision accuracy (DA) over the previous Bayesian model and CR models.
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Hassan, Syed Karimuddin and Syed Muhammad. "Defect Detection in SRS using Requirement Defect Taxonomy." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5253.
Повний текст джерелаАнотація:
Context: Defects occurred in the SRS may cause problems in project due to implementation of poor requirements which require extra time, effort, resources and budget to complete it. Reading techniques i.e., checklist based reading (CBR) helps to guide reviewers in identifying defects in software requirement specification (SRS) during individual requirement inspections. Checklists contain potential defects/problems to look for, but often lack clear definitions with examples of the problem, and also their abstractions are different. Therefore, there is a need for identifying existing defects and classifiers and to create a consolidated version of taxonomy. Objectives: We developed taxonomy for requirement defects that are in requirement specifications and compared it with the checklist based approach. The main objective was to investigate and compare the effectiveness and efficiency of inspection techniques (checklist and taxonomy) with M.Sc. software engineering students and industry practitioners by performing a both controlled student and industry experiment. Methods: Literature review, controlled student experiment and controlled industry experiment were the research methods utilized to fulfill the objectives of this study. INSPEC and Google scholar database was used to find the articles from the literature. Controlled student experiment was conducted with the M.Sc. software engineering students and controlled industry experiment was performed with the industry practitioners to evaluate the effectiveness and efficiency of the two treatments that are checklist and taxonomy. Results: An extensive literature review helped us to identify several types of defects with their definitions and examples. In this study, we studied various defect classifiers, checklists, requirement defects and inspection techniques and then built taxonomy for requirement defects. We evaluated whether the taxonomy performed better with respect to checklist using controlled experiments with students and practitioners. Moreover, the results of student experiment (p= 0.90 for effectiveness and p=0.10 for efficiency) and practitioner experiment (p=1.0 for effectiveness and p=0.70 for efficiency) did not show significant values with respect to effectiveness and efficiency. But because of less number of practitioners it is not possible to apply a statistical test since we also have used standard formulas to calculate effectiveness and efficiency. 2 out of the 3 reviewers using taxonomy found more defect types compared to 3 reviewers using checklist. 10-15% more defects have been found by reviewers using taxonomy. 2 out of the 3 reviewers using taxonomy are more productive (measuring in hours) compared to reviewers of checklist. Although the results are quite better than the student experiment but it is hard to claim that reviewers using taxonomy are more effective and efficient than the reviewers using checklist because of less subjects in number. The results of the post experiment questionnaire revealed that the taxonomy is easy to use and easy to understand but hard to remember while inspecting SRS than the checklist technique. Conclusions: Previously researchers created taxonomies for their own purpose or on industry demand. These taxonomies lack clear and understandable definitions. To overcome this problem, we built taxonomy with requirement defects which consists of definitions and examples. No claims are made based on student experiment because of insignificant values with respect to effectiveness and efficiency. Although the controlled industry experiment results showed that taxonomy performed slightly better than the checklist in efficiency i.e., in defect detection rate and effectiveness i.e., number of defect found. From this we can conclude that taxonomy helps guiding the reviewers to indentify defects from SRS but not quite much so it is recommended to perform a further study with practitioners in a large scale for effective results.skarimuddin@yahoo.com, hassanshah357@gmail.com
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Porto, Faimison Rodrigues. "Cross-project defect prediction with meta-Learning." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21032018-163840/.
Повний текст джерелаАнотація:
Defect prediction models assist tester practitioners on prioritizing the most defect-prone parts of the software. The approach called Cross-Project Defect Prediction (CPDP) refers to the use of known external projects to compose the training set. This approach is useful when the amount of historical defect data of a company to compose the training set is inappropriate or insufficient. Although the principle is attractive, the predictive performance is a limiting factor. In recent years, several methods were proposed aiming at improving the predictive performance of CPDP models. However, to the best of our knowledge, there is no evidence of which CPDP methods typically perform best. Moreover, there is no evidence on which CPDP methods perform better for a specific application domain. In fact, there is no machine learning algorithm suitable for all domains. The decision task of selecting an appropriate algorithm for a given application domain is investigated in the meta-learning literature. A meta-learning model is characterized by its capacity of learning from previous experiences and adapting its inductive bias dynamically according to the target domain. In this work, we investigate the feasibility of using meta-learning for the recommendation of CPDP methods. In this thesis, three main goals were pursued. First, we provide an experimental analysis to investigate the feasibility of using Feature Selection (FS) methods as an internal procedure to improve the performance of two specific CPDP methods. Second, we investigate which CPDP methods present typically best performances. We also investigate whether the typically best methods perform best for the same project datasets. The results reveal that the most suitable CPDP method for a project can vary according to the project characteristics, which leads to the third investigation of this work. We investigate the several particularities inherent to the CPDP context and propose a meta-learning solution able to learn from previous experiences and recommend a suitable CDPD method according to the characteristics of the project being predicted. We evaluate the learning capacity of the proposed solution and its performance in relation to the typically best CPDP methods.Modelos de predição de defeitos auxiliam profissionais de teste na priorização de partes do software mais propensas a conter defeitos. A abordagem de predição de defeitos cruzada entre projetos (CPDP) refere-se à utilização de projetos externos já conhecidos para compor o conjunto de treinamento. Essa abordagem é útil quando a quantidade de dados históricos de defeitos é inapropriada ou insuficiente para compor o conjunto de treinamento. Embora o princípio seja atrativo, o desempenho de predição é um fator limitante nessa abordagem. Nos últimos anos, vários métodos foram propostos com o intuito de melhorar o desempenho de predição de modelos CPDP. Contudo, na literatura, existe uma carência de estudos comparativos que apontam quais métodos CPDP apresentam melhores desempenhos. Além disso, não há evidências sobre quais métodos CPDP apresentam melhor desempenho para um domínio de aplicação específico. De fato, não existe um algoritmo de aprendizado de máquina que seja apropriado para todos os domínios de aplicação. A tarefa de decisão sobre qual algoritmo é mais adequado a um determinado domínio de aplicação é investigado na literatura de meta-aprendizado. Um modelo de meta-aprendizado é caracterizado pela sua capacidade de aprender a partir de experiências anteriores e adaptar seu viés de indução dinamicamente de acordo com o domínio alvo. Neste trabalho, nós investigamos a viabilidade de usar meta-aprendizado para a recomendação de métodos CPDP. Nesta tese são almejados três principais objetivos. Primeiro, é conduzida uma análise experimental para investigar a viabilidade de usar métodos de seleção de atributos como procedimento interno de dois métodos CPDP, com o intuito de melhorar o desempenho de predição. Segundo, são investigados quais métodos CPDP apresentam um melhor desempenho em um contexto geral. Nesse contexto, também é investigado se os métodos com melhor desempenho geral apresentam melhor desempenho para os mesmos conjuntos de dados (ou projetos de software). Os resultados revelam que os métodos CPDP mais adequados para um projeto podem variar de acordo com as características do projeto sendo predito. Essa constatação conduz à terceira investigação realizada neste trabalho. Foram investigadas as várias particularidades inerentes ao contexto CPDP a fim de propor uma solução de meta-aprendizado capaz de aprender com experiências anteriores e recomendar métodos CPDP adequados, de acordo com as características do software. Foram avaliados a capacidade de meta-aprendizado da solução proposta e a sua performance em relação aos métodos base que apresentaram melhor desempenho geral.
5
Tran, Qui Can Cuong. "Empirical evaluation of defect identification indicators and defect prediction models." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2553.
Повний текст джерелаАнотація:
Context. Quality assurance plays a vital role in the software engineering development process. It can be considered as one of the activities, to observe the execution of software project to validate if it behaves as expected or not. Quality assurance activities contribute to the success of software project by reducing the risks of software’s quality. Accurate planning, launching and controlling quality assurance activities on time can help to improve the performance of software projects. However, quality assurance activities also consume time and cost. One of the reasons is that they may not focus on the potential defect-prone area. In some of the latest and more accurate findings, researchers suggested that quality assurance activities should focus on the scope that may have the potential of defect; and defect predictors should be used to support them in order to save time and cost. Many available models recommend that the project’s history information be used as defect indicator to predict the number of defects in the software project. Objectives. In this thesis, new models are defined to predict the number of defects in the classes of single software systems. In addition, the new models are built based on the combination of product metrics as defect predictors. Methods. In the systematic review a number of article sources are used, including IEEE Xplore, ACM Digital Library, and Springer Link, in order to find the existing models related to the topic. In this context, open source projects are used as training sets to extract information about occurred defects and the system evolution. The training data is then used for the definition of the prediction models. Afterwards, the defined models are applied on other systems that provide test data, so information that was not used for the training of the models; to validate the accuracy and correctness of the models Results. Two models are built. One model is built to predict the number of defects of one class. One model is built to predict whether one class contains bug or no bug.. Conclusions. The proposed models are the combination of product metrics as defect predictors that can be used either to predict the number of defects of one class or to predict if one class contains bugs or no bugs. This combination of product metrics as defect predictors can improve the accuracy of defect prediction and quality assurance activities; by giving hints on potential defect prone classes before defect search activities will be performed. Therefore, it can improve the software development and quality assurance in terms of time and cost
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Sherwood, Patricia Ann. "Inspections : software development process for building defect free software applied in a small-scale software development environment /." Online version of thesis, 1990. http://hdl.handle.net/1850/10598.
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Hameed, Muhammad Muzaffar, and Muhammad Zeeshan ul Haq. "DefectoFix : An interactive defect fix logging tool." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5268.
Повний текст джерелаАнотація:
Despite the large efforts made during the development phase to produce fault free system, most of the software implementations still require the testing of entire system. The main problem in the software testing is the automation that could verify the system without manual intervention. Recent work in software testing is related to the automated fault injection by using fault models from repository. This requires a lot of efforts, which adds to the complexity of the system. To solve this issue, this thesis suggests DefectoFix framework. DefectoFix is an interactive defect fix logging tools that contains five components namely Version Control Sysem (VCS), source code files, differencing algorithm, Defect Fix Model (DFM) creation and additional information (project name, class name, file name, revision number, diff model). The proposed differencing algorithm extracts detailed information by detecting differences in source code files. This algorithm performs comparison at sub-tree levels of source code files. The extracted differences with additional information are stored as DFM in repository. DFM(s) can later be used for the automated fault injection process. The validation of DefectoFix framework is performed by a tool developed using Ruby programming language. Our case study confirms that the proposed framework generates a correct DFM and is useful in automated fault injection and software validation activities.
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Phaphoom, Nattakarn. "Pair Programming and Software Defects : A Case Study." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3513.
Повний текст джерелаАнотація:
Pair programming is a programming technique in which two programmers sit literally side by side working on the same task at the same computer. One member of a pair called “driver” is in charge of writing the code. The other member plays a role of “navigator”, working on the more strategic tasks, such as looking for tactical error, thinking about overall structure, and finding better alternatives. Pair programming is claimed to improve product quality, reduce defects, and shorten time to market. On the other hand, it has been criticized on cost efficiency. To increase a body of evidence regarding the real benefits of pair programming, this thesis investigates its effect on software defects and efficiency of defect correction. The analysis bases on 14-month data of project artifacts and developers' activities collected from a large Italian manufacturing company. The team of 16 developers adopts a customized version of extreme programming and practices pair programming on a daily basis. We investigate sources of defects and defect correction activities of approximately 8% of defects discovered during that time, and enhancement activities of approximately 9% of new requirements. Then we analyze whether there exists an effect of pair programming on defect rate, duration and effort of defect correction, and precision of localizing defects. The result shows that pair programming reduces the introduction of new defects when the code needs to be modified for defect corrections and enhancements.
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Akinwale, Olusegun. "DuoTracker tool support for software defect data collection and analysis /." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447633.
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Gray, David Philip Harry. "Software defect prediction using static code metrics : formulating a methodology." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/11067.
Повний текст джерелаАнотація:
Software defect prediction is motivated by the huge costs incurred as a result of software failures. In an effort to reduce these costs, researchers have been utilising software metrics to try and build predictive models capable of locating the most defect-prone parts of a system. These areas can then be subject to some form of further analysis, such as a manual code review. It is hoped that such defect predictors will enable software to be produced more cost effectively, and/or be of higher quality. In this dissertation I identify many data quality and methodological issues in previous defect prediction studies. The main data source is the NASA Metrics Data Program Repository. The issues discovered with these well-utilised data sets include many examples of seemingly impossible values, and much redundant data. The redundant, or repeated data points are shown to be the cause of potentially serious data mining problems. Other methodological issues discovered include the violation of basic data mining principles, and the misleading reporting of classifier predictive performance. The issues discovered lead to a new proposed methodology for software defect prediction. The methodology is focused around data analysis, as this appears to have been overlooked in many prior studies. The aim of the methodology is to be able to obtain a realistic estimate of potential real-world predictive performance, and also to have simple performance baselines with which to compare against the actual performance achieved. This is important as quantifying predictive performance appropriately is a difficult task. The findings of this dissertation raise questions about the current defect prediction body of knowledge. So many data-related and/or methodological errors have previously occurred that it may now be time to revisit the fundamental aspects of this research area, to determine what we really know, and how we should proceed.
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Portnoy, William. "Distributable defect localization using Markov models /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6883.
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Bowes, David Hutchinson. "Factors affecting the performance of trainable models for software defect prediction." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/10978.
Повний текст джерелаАнотація:
Context. Reports suggest that defects in code cost the US in excess of $50billion per year to put right. Defect Prediction is an important part of Software Engineering. It allows developers to prioritise the code that needs to be inspected when trying to reduce the number of defects in code. A small change in the number of defects found will have a significant impact on the cost of producing software. Aims. The aim of this dissertation is to investigate the factors which a ect the performance of defect prediction models. Identifying the causes of variation in the way that variables are computed should help to improve the precision of defect prediction models and hence improve the cost e ectiveness of defect prediction. Methods. This dissertation is by published work. The first three papers examine variation in the independent variables (code metrics) and the dependent variable (number/location of defects). The fourth and fifth papers investigate the e ect that di erent learners and datasets have on the predictive performance of defect prediction models. The final paper investigates the reported use of di erent machine learning approaches in studies published between 2000 and 2010. Results. The first and second papers show that independent variables are sensitive to the measurement protocol used, this suggests that the way data is collected a ects the performance of defect prediction. The third paper shows that dependent variable data may be untrustworthy as there is no reliable method for labelling a unit of code as defective or not. The fourth and fifth papers show that the dataset and learner used when producing defect prediction models have an e ect on the performance of the models. The final paper shows that the approaches used by researchers to build defect prediction models is variable, with good practices being ignored in many papers. Conclusions. The measurement protocols for independent and dependent variables used for defect prediction need to be clearly described so that results can be compared like with like. It is possible that the predictive results of one research group have a higher performance value than another research group because of the way that they calculated the metrics rather than the method of building the model used to predict the defect prone modules. The machine learning approaches used by researchers need to be clearly reported in order to be able to improve the quality of defect prediction studies and allow a larger corpus of reliable results to be gathered.
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Liljeson, Mattias, and Alexander Mohlin. "Software defect prediction using machine learning on test and source code metrics." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4162.
Повний текст джерелаАнотація:
Context. Software testing is the process of finding faults in software while executing it. The results of the testing are used to find and correct faults. Software defect prediction estimates where faults are likely to occur in source code. The results from the defect prediction can be used to opti- mize testing and ultimately improve software quality. Machine learning, that concerns computer programs learning from data, is used to build pre- diction models which then can be used to classify data. Objectives. In this study we, in collaboration with Ericsson, investigated whether software metrics from source code files combined with metrics from their respective tests predicts faults with better prediction perfor- mance compared to using only metrics from the source code files. Methods. A literature review was conducted to identify inputs for an ex- periment. The experiment was applied on one repository from Ericsson to identify the best performing set of metrics. Results. The prediction performance results of three metric sets are pre- sented and compared with each other. Wilcoxon’s signed rank tests are performed on four different performance measures for each metric set and each machine learning algorithm to demonstrate significant differences of the results. Conclusions. We conclude that metrics from tests can be used to predict faults. However, the combination of source code metrics and test metrics do not outperform using only source code metrics. Moreover, we conclude that models built with metrics from the test metric set with minimal infor- mation of the source code can in fact predict faults in the source code.
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Mahmood, Zaheed. "An analysis of software defect prediction studies through reproducibility and replication." Thesis, University of Hertfordshire, 2018. http://hdl.handle.net/2299/20826.
Повний текст джерелаАнотація:
Context. Software defect prediction is essential in reducing software development costs and in helping companies save their reputation. Defect prediction uses mathematical models to identify patterns associated with defects within code. Resources spent reviewing the entire code can be minimised by focusing on defective parts of the code. Recent findings suggest many published prediction models may not be reliable. Critical scientific methods for identifying reliable research are Replication and Reproduction. Replication can test the external validity of studies while Reproduction can test their internal validity. Aims. The aims of my dissertation are first to study the use and quality of replications and reproductions in defect prediction. Second, to identify factors that aid or hinder these scientific methods. Methods. My methodology is based on tracking the replication of 208 defect prediction studies identified in a highly cited Systematic Literature Review (SLR) [Hall et al. 2012]. I analyse how often each of these 208 studies has been replicated and determine the type of replication carried out. I use quality, citation counts, publication venue, impact factor, and data availability from all the 208 papers to see if any of these factors are associated with the frequency with which they are replicated. I further reproduce the original studies that have been replicated in order to check their internal validity. Finally, I identify factors that affect reproducibility. Results. Only 13 (6%) of the 208 studies are replicated, most of which fail a quality check. Of the 13 replicated original studies, 62% agree with their replications and 38% disagree. The main feature of a study associated with being replicated is that original papers appear in the Transactions of Software Engineering (TSE) journal. The number of citations an original paper had was also an indicator of the probability of being replicated. In addition, studies conducted using closed source data have more replications than those based on open source data. Of the 4 out of 5 papers I reproduced, their results differed with those of the original by more than 5%. Four factors are likely to have caused these failures: i) lack of a single version of the data initially used by the original; ii) the different dataset versions available have different properties that impact model performance; iii) unreported data preprocessing; and iv) inconsistent results from alternative versions of the same tools. Conclusions. Very few defect prediction studies are replicated. The lack of replication and failure of reproduction means that it remains unclear how reliable defect prediction is. Further investigation into this failure provides key aspects researchers need to consider when designing primary studies, performing replication and reproduction studies. Finally, I provide practical steps for improving the likelihood of replication and the chances of validating a study by reporting key factors.
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Curhan, Lisa A. 1961. "Software defect tracking during new product development of a computer system." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34824.
Повний текст джерелаАнотація:
Thesis (S.M.)--Massachusetts Institute of Technology, System Design & Management Program, 2005.Includes bibliographical references (p. 74-75).
Software defects (colloquially known as "bugs") have a major impact on the market acceptance and profitability of computer systems. Sun Microsystems markets both hardware and software for a wide variety of customer needs. The integration of hardware and software is a key core capability for Sun. Minimizing the quantity and impact of software defects on this integration during new product development is essential to execution of a timely and high-quality product. To analyze the effect of software defects on the product development cycle for a midrange computer system, I have used a particular computer platform, the Productl server, as a case study. The objective of this work was to use Sun's extensive database of software defects as a source for data-mining in order to draw conclusions about the types of software defects that tend to occur during new product development and early production ramp. I also interviewed key players on the Productl development team for more insight into the causes and impacts of software defects for this platform. Some of the major themes that resulted from this study include: The impact of defects is not necessarily proportional to their quantity. Some types of defects have a much higher cost to fix due to customer impact, time needed to fix, or the wide distribution of the software in which they are embedded. Software Requirements need to be vetted extensively before production of new code. This is especially critical for platform-specific requirements. The confluence of new features, new software structure and new hardware can lead to a greater density of software defects. The higher number of defects associated with the new System Controller code supports this conclusion. Current Limitations of Defect Data Mining: Automated extraction
(cont.) of information is most efficient when it can be applied to numbers and short text strings. However, the evaluation of software defects for root cause cannot be easily summarized in a few words or numbers. Therefore, an intelligent classification methodology for root causes of software defects, to be included in Sun's defect database, would be extremely useful to increase the utility of the database for institutional learning. Software Defect Data Mining seems to be underutilized at Sun. I have barely touched the surface of the information that can be extracted from our "BugDB" defect database. This data resource is rich with history. We should extract and analyze this type of data frequently.
by Lisa A. Curhan.
S.M.
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Isunza, Navarro Abgeiba Yaroslava. "Evaluation of Attention Mechanisms for Just-In-Time Software Defect Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288724.
Повний текст джерелаАнотація:
Just-In-Time Software Defect Prediction (JIT-DP) focuses on predicting errors in software at change-level with the objective of helping developers identify defects while the development process is still ongoing, and improving the quality of software applications. This work studies deep learning techniques by applying attention mechanisms that have been successful in, among others, Natural Language Processing (NLP) tasks. We introduce two networks named Convolutional Neural Network with Bidirectional Attention (BACNN) and Bidirectional Attention Code Network (BACoN) that employ a bi-directional attention mechanism between the code and message of a software change. Furthermore, we examine BERT [17] and RoBERTa [57] attention architectures for JIT-DP. More specifically, we study the effectiveness of the aforementioned attention-based models to predict defective commits compared to the current state of the art, DeepJIT [37] and TLEL [101]. Our experiments evaluate the models by using software changes from the OpenStack open source project. The results showed that attention-based networks outperformed the baseline models in terms of accuracy in the different evaluation settings. The attention-based models, particularly BERT and RoBERTa architectures, demonstrated promising results in identifying defective software changes and proved to be effective in predicting defects in changes of new software releases.Just-In-Time Defect Prediction (JIT-DP) fokuserar på att förutspå fel i mjukvara vid ändringar i koden, med målet att hjälpa utvecklare att identifiera defekter medan utvecklingsprocessen fortfarande är pågående, och att förbättra kvaliteten hos applikationsprogramvara. Detta arbete studerar djupinlärningstekniker genom att tillämpa attentionmekanismer som har varit framgångsrika inom, bland annat, språkteknologi (NLP). Vi introducerar två nätverk vid namn Convolutional Neural Network with Bidirectional Attention (BACNN), och Bidirectional Attention Code Network (BACoN), som använder en tvåriktad attentionmekanism mellan koden och meddelandet om en mjukvaruändring. Dessutom undersöker vi BERT [17] och RoBERTa [57], attentionarkitekturer för JIT-DP. Mer specifikt studerar vi hur effektivt dessa attentionbaserade modeller kan förutspå defekta ändringar, och jämför dem med de bästa tillgängliga arkitekturerna DeePJIT [37] och TLEL [101]. Våra experiment utvärderar modellerna genom att använda mjukvaruändringar från det öppna källkodsprojektet OpenStack. Våra resultat visar att attentionbaserade nätverk överträffar referensmodellen sett till träffsäkerheten i de olika scenarierna. De attentionbaserade modellerna, framför allt BERT och RoBERTa, demonstrerade lovade resultat när det kommer till att identifiera defekta mjukvaruändringar och visade sig vara effektiva på att förutspå defekter i ändringar av nya mjukvaruversioner.
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Shippey, Thomas Joshua. "Exploiting abstract syntax trees to locate software defects." Thesis, University of Hertfordshire, 2015. http://hdl.handle.net/2299/16365.
Повний текст джерелаАнотація:
Context. Software defect prediction aims to reduce the large costs involved with faults in a software system. A wide range of traditional software metrics have been evaluated as potential defect indicators. These traditional metrics are derived from the source code or from the software development process. Studies have shown that no metric clearly out performs another and identifying defect-prone code using traditional metrics has reached a performance ceiling. Less traditional metrics have been studied, with these metrics being derived from the natural language of the source code. These newer, less traditional and finer grained metrics have shown promise within defect prediction. Aims. The aim of this dissertation is to study the relationship between short Java constructs and the faultiness of source code. To study this relationship this dissertation introduces the concept of a Java sequence and Java code snippet. Sequences are created by using the Java abstract syntax tree. The ordering of the nodes within the abstract syntax tree creates the sequences, while small sub sequences of this sequence are the code snippets. The dissertation tries to find a relationship between the code snippets and faulty and non-faulty code. This dissertation also looks at the evolution of the code snippets as a system matures, to discover whether code snippets significantly associated with faulty code change over time. Methods. To achieve the aims of the dissertation, two main techniques have been developed; finding defective code and extracting Java sequences and code snippets. Finding defective code has been split into two areas - finding the defect fix and defect insertion points. To find the defect fix points an implementation of the bug-linking algorithm has been developed, called S + e . Two algorithms were developed to extract the sequences and the code snippets. The code snippets are analysed using the binomial test to find which ones are significantly associated with faulty and non-faulty code. These techniques have been performed on five different Java datasets; ArgoUML, AspectJ and three releases of Eclipse.JDT.core Results. There are significant associations between some code snippets and faulty code. Frequently occurring fault-prone code snippets include those associated with identifiers, method calls and variables. There are some code snippets significantly associated with faults that are always in faulty code. There are 201 code snippets that are snippets significantly associated with faults across all five of the systems. The technique is unable to find any significant associations between code snippets and non-faulty code. The relationship between code snippets and faults seems to change as the system evolves with more snippets becoming fault-prone as Eclipse.JDT.core evolved over the three releases analysed. Conclusions. This dissertation has introduced the concept of code snippets into software engineering and defect prediction. The use of code snippets offers a promising approach to identifying potentially defective code. Unlike previous approaches, code snippets are based on a comprehensive analysis of low level code features and potentially allow the full set of code defects to be identified. Initial research into the relationship between code snippets and faults has shown that some code constructs or features are significantly related to software faults. The significant associations between code snippets and faults has provided additional empirical evidence to some already researched bad constructs within defect prediction. The code snippets have shown that some constructs significantly associated with faults are located in all five systems, and although this set is small finding any defect indicators that transfer successfully from one system to another is rare.
18
Siahaan, Antony. "Defect correction based domain decomposition methods for some nonlinear problems." Thesis, University of Greenwich, 2011. http://gala.gre.ac.uk/7144/.
Повний текст джерелаАнотація:
Defect correction schemes as a class of nonoverlapping domain decomposition methods offer several advantages in the ways they split a complex problem into several subdomain problems with less complexity. The schemes need a nonlinear solver to take care of the residual at the interface. The adaptive-∝ solver can converge locally in the ∞-norm, where the sufficient condition requires a relatively small local neighbourhood and the problem must have a strongly diagonal dominant Jacobian matrix with a very small condition number. Yet its advantage can be of high signicance in the computational cost where it simply needs a scalar as the approximation of Jacobian matrix. Other nonlinear solvers employed for the schemes are a Newton-GMRES method, a Newton method with a finite difference Jacobian approximation, and nonlinear conjugate gradient solvers with Fletcher-Reeves and Pollak-Ribiere searching direction formulas. The schemes are applied to three nonlinear problems. The first problem is a heat conduction in a multichip module where there the domain is assembled from many components of different conductivities and physical sizes. Here the implementations of the schemes satisfy the component meshing and gluing concept. A finite difference approximation of the residual of the governing equation turns out to be a better defect equation than the equality of normal derivative. Of all the nonlinear solvers implemented in the defect correction scheme, the nonlinear conjugate gradient method with Fletcher-Reeves searching direction has the best performance. The second problem is a 2D single-phase fluid flow with heat transfer where the PHOENICS CFD code is used to run the subdomain computation. The Newton method with a finite difference Jacobian is a reasonable interface solver in coupling these subdomain computations. The final problem is a multiphase heat and moisture transfer in a porous textile. The PHOENICS code is also used to solve the system of partial differential equations governing the multiphase process in each subdomain while the coupling of the subdomain solutions is taken care of with some FORTRAN codes by the defect correction schemes. A scheme using a modified-∝ method fails to obtain decent solutions in both single and two layers case. On the other hand, the scheme using the above Newton method produces satisfying results for both cases where it can lead an initially distant interface data into a good convergent solution. However, it is found that in general the number of nonlinear iteration of the defect correction schemes increases with the mesh refinement.
19
Land, Lesley Pek Wee Information Systems Technology & Management Australian School of Business UNSW. "Software group reviews and the impact of procedural roles on defect detection performance." Awarded by:University of New South Wales. School of Information Systems, Technology and Management, 2000. http://handle.unsw.edu.au/1959.4/21838.
Повний текст джерелаАнотація:
Software reviews (inspections) have received widespread attention for ensuring the quality of software, by finding and repairing defects in software products. A typical review process consists of two stages critical for defect detection: individual review followed by group review. This thesis addresses two attributes to improve our understanding of the task model: (1) the need for review meetings, and (2) the use of roles in meetings. The controversy of review meeting effectiveness has been consistently raised in the literature. Proponents maintain that the review meeting is the crux of the review process, resulting in group synergism and qualitative benefits (e.g. user satisfaction). Opponents argue that against meetings because the costs of organising and conducting them are high, and there is no net meeting gain. The persistence of these diverse views is the main motivation behind this thesis. Although commonly prescribed in meetings, roles have not yet been empirically validated. Three procedural roles (moderator, reader, recorder) were considered. A conceptual framework on software reviews was developed, from which main research questions were identified. Two experiments were conducted. Review performance was operationalised in terms of true defects and false positives. The review product was COBOL code. The results indicated that in terms of true defects, group reviews outperformed the average individual but not nominal group reviews (aggregate of individual reviews). However, groups have the ability to filter false positives from the individuals' findings. Roles provided limited benefits in improving group reviews. Their main function is to reduce process loss, by encouraging systematic consideration of the individuals' findings. When two or more reviewers find a defect during individual reviews, it is likely to be carried through to the meeting (plurality effect). Groups employing roles reported more 'new' false positives (not identified from preparation) than groups without roles. Overall, subjects' ability at the defect detection was low. This thesis suggests that reading technologies may be helpful for improving reviewer performance. The inclusion of an author role may also reduce the level of false positive detection. The results have implications on the design and support of the software review process.
20
DUTTA, BINAMRA. "Enterprise Software Metrics: How To Add Business Value." Kent State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=kent1239239432.
Повний текст джерела
21
Sun, Boya. "PRECISION IMPROVEMENT AND COST REDUCTION FOR DEFECT MINING AND TESTING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1321827962.
Повний текст джерела
22
Kasianenko, Stanislav. "Predicting Software Defectiveness by Mining Software Repositories." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78729.
Повний текст джерелаАнотація:
One of the important aims of the continuous software development process is to localize and remove all existing program bugs as fast as possible. Such goal is highly related to software engineering and defectiveness estimation. Many big companies started to store source code in software repositories as the later grew in popularity. These repositories usually include static source code as well as detailed data for defects in software units. This allows analyzing all the data without interrupting programing process. The main problem of large, complex software is impossibility to control everything manually while the price of the error can be very high. This might result in developers missing defects on testing stage and increase of maintenance cost. The general research goal is to find a way of predicting future software defectiveness with high precision. Reducing maintenance and development costs will contribute to reduce the time-to-market and increase software quality. To address the problem of estimating residual defects an approach was found to predict residual defectiveness of a software by the means of machine learning. For a prime machine learning algorithm, a regression decision tree was chosen as a simple and reliable solution. Data for this tree is extracted from static source code repository and divided into two parts: software metrics and defect data. Software metrics are formed from static code and defect data is extracted from reported issues in the repository. In addition to already reported bugs, they are augmented with unreported bugs found on “discussions” section in repository and parsed by a natural language processor. Metrics were filtered to remove ones, that were not related to defect data by applying correlation algorithm. Remaining metrics were weighted to use the most correlated combination as a training set for the decision tree. As a result, built decision tree model allows to forecast defectiveness with 89% chance for the particular product. This experiment was conducted using GitHub repository on a Java project and predicted number of possible bugs in a single file (Java class). The experiment resulted in designed method for predicting possible defectiveness from a static code of a single big (more than 1000 files) software version.
23
Saxena, Kaustubh. "Investigation of the Effect of the Number of Inspectors on the Software Defect Estimates." Thesis, North Dakota State University, 2012. https://hdl.handle.net/10365/26714.
Повний текст джерелаАнотація:
Capture-recapture models help software managers by providing post-inspection defect estimate remaining in a software artifact to determine if a re-inspection in necessary. These estimates are calculated using the number of unique faults per inspector and the overlap of faults found by inspectors during an inspection cycle. A common belief is that the accuracy of the capture-recapture estimates improves with the inspection team size. This however, has not been empirically studied. This paper empirically investigates the effect of the number of inspectors on the estimates produced by capture-recapture models, by using inspection data with varying number and types of inspectors. The results show that the SC (Sample Coverage) estimators are best suited to software inspections and need least number of inspectors to achieve accurate and precise estimates. Our results also provide a detailed analysis of the number of inspectors necessary to obtain estimates within 5-20% of the actual defect count.
24
Kristiansen, Jan Maximilian Winther. "Software Defect Analysis : An Empirical Study of Causes and Costs in the Information Technology Industry." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11120.
Повний текст джерелаАнотація:
The area of software defects is not thoroughly studied in current research, even though it is estimated to be one of the most expensive topics in industries. Hence, certain researchers characterise the lack of research as a scandal within software engineering. Little research has been performed in investigating the root causes of defects, even thought we have classification schemes which aims to classify the what, where and why regarding software defects. We want to investigate the root causes of software defects through both qualitative and quantitative methods.We collected defect reports from three different types of projects in the defect tracking system of Company X. The first project was a project concerned with development of a general core of functionality which other projects could use. The second was a project aim at the mass-software market, while the third project was tailored software to a the needs of a client. These defect reports were analysed by both qualitative and quantitative methods. The qualitative methods were based on grounded theory. The methods tried to establish a theory of why some defect require extensive effort to correct through analysis of the discussions in the defect reports. The quantitative methods were used to describe differences between defects which required extensive or little effort to correct.In the qualitative analysis, we found four main root causes which explain why a group of defects require extensive effort to correct: hard to determine the location of the defect, long discussion or clarification of the defect, incorrect corrections introduces new defects, and implementation of missing functionality or re-implementation of existing functionality. A comparison between the four root causes and project types revealed the root causes were influenced by the project types. The first project had a larger degree of discussion and incorrect corrections than the second and third projects. The second and third projects were more concerned with hard to locate defects and implementation of missing functionality or re-implementation of functionality. Similarly, a comparison against another organisation showed there were differences with regard to root causes for extensive effort. This showed how systematic analysis of defect reports can yield software process improvement opportunities.In the quantitative analysis, we found differences among extensive or little effort to correct defects and project types. The extensive to correct defects of the first project were due to incorrect algorithms or methods, injected during the design phase, and high risk of regressions. In the second project, the extensive effort to correct defects were due to algorithms, methods, functions, classes and objects, were concerned with the core, platform, and user interface layers and injected during the design phase, and lower regression risks. In the third project, the defects which required extensive effort to correct were due to assignation and initialisation of variables, or function, classes and objects, related to the core-layer, injected during the coding phase, and average regression risk of medium. The little effort to correct defects in the core project were concerned with assignation or initialisation of variables, checking statements, lower regression risk, injected during the code phase. In the second project, easy to correct defects were concerned with checking statements in the code which had a low regression risk. In the third project, defects which required little effort to correct were due to checking statements, interfaces with third party libraries, lower regression risk and stem from requirements. The quantitative analysis contained high levels of unspecified values for little effort to correct defect. The levels of unspecified attributes were lower for defects which required extensive effort to correct.We concluded there were differences among project types with regard to root causes for defects, and that there were differences similar between different levels of effort required to correct defects. However, the study were not able to measure how these differences influenced the root causes as the study was performed in a descriptive manner.
25
Vandehei, Bailey R. "Leveraging Defects Life-Cycle for Labeling Defective Classes." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2111.
Повний текст джерелаАнотація:
Data from software repositories are a very useful asset to building dierent kinds of
models and recommender systems aimed to support software developers. Specically,
the identication of likely defect-prone les (i.e., classes in Object-Oriented systems)
helps in prioritizing, testing, and analysis activities. This work focuses on automated
methods for labeling a class in a version as defective or not. The most used methods
for automated class labeling belong to the SZZ family and fail in various circum-
stances. Thus, recent studies suggest the use of aect version (AV) as provided by
developers and available in the issue tracker such as JIRA. However, in many cir-
cumstances, the AV might not be used because it is unavailable or inconsistent. The
aim of this study is twofold: 1) to measure the AV availability and consistency in
open-source projects, 2) to propose, evaluate, and compare to SZZ, a new method
for labeling defective classes which is based on the idea that defects have a stable
life-cycle in terms of proportion of versions needed to discover the defect and to x
the defect. Results related to 212 open-source projects from the Apache ecosystem,
featuring a total of about 125,000 defects, show that the AV cannot be used in the
majority (51%) of defects. Therefore, it is important to investigate automated meth-
ods for labeling defective classes. Results related to 76 open-source projects from the
Apache ecosystem, featuring a total of about 6,250,000 classes that are are aected
by 60,000 defects and spread over 4,000 versions and 760,000 commits, show that the
proposed method for labeling defective classes is, in average among projects and de-
fects, more accurate, in terms of Precision, Kappa, F1 and MCC than all previously
proposed SZZ methods. Moreover, the improvement in accuracy from combining SZZ
with defects life-cycle information is statistically signicant but practically irrelevant
(
overall and in average, more accurate via defects' life-cycle than any SZZ method.
26
Ouyang, Sheng. "The effect of amount of software reuse on defect severity in real-time C-base environment." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ55266.pdf.
Повний текст джерела
27
Shams, Zalia. "Automated Assessment of Student-written Tests Based on Defect-detection Capability." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52024.
Повний текст джерелаАнотація:
Software testing is important, but judging whether a set of software tests is effective is difficult. This problem also appears in the classroom as educators more frequently include software testing activities in programming assignments. The most common measures used to assess student-written software tests are coverage criteria—tracking how much of the student’s code (in terms of statements, or branches) is exercised by the corresponding tests. However, coverage criteria have limitations and sometimes overestimate the true quality of the tests. This dissertation investigates alternative measures of test quality based on how many defects the tests can detect either from code written by other students—all-pairs execution—or from artificially injected changes—mutation analysis. We also investigate a new potential measure called checked code coverage that calculates coverage from the dynamic backward slices of test oracles, i.e. all statements that contribute to the checked result of any test. Adoption of these alternative approaches in automated classroom grading systems require overcoming a number of technical challenges. This research addresses these challenges and experimentally compares different methods in terms of how well they predict defect-detection capabilities of student-written tests when run against over 36,500 known, authentic, human-written errors. For data collection, we use CS2 assignments and evaluate students’ tests with 10 different measures—all-pairs execution, mutation testing with four different sets of mutation operators, checked code coverage, and four coverage criteria. Experimental results encompassing 1,971,073 test runs show that all-pairs execution is the most accurate predictor of the underlying defect-detection capability of a test suite. The second best predictor is mutation analysis with the statement deletion operator. Further, no strong correlation was found between defect-detection capability and coverage measures.Ph. D.
28
Wilkerson, Jerod W. "Closing the Defect Reduction Gap between Software Inspection and Test-Driven Development: Applying Mutation Analysis to Iterative, Test-First Programming." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/195160.
Повний текст джерелаАнотація:
The main objective of this dissertation is to assist in reducing the chaotic state of the software engineering discipline by providing insights into both the effectiveness of software defect reduction methods and ways these methods can be improved. The dissertation is divided into two main parts. The first is a quasi-experiment comparing the software defect rates and initial development costs of two methods of software defect reduction: software inspection and test-driven development (TDD). Participants, consisting of computer science students at the University of Arizona, were divided into four treatment groups and were asked to complete the same programming assignment using either TDD, software inspection, both, or neither. Resulting defect counts and initial development costs were compared across groups. The study found that software inspection is more effective than TDD at reducing defects, but that it also has a higher initial cost of development. The study establishes the existence of a defect-reduction gap between software inspection and TDD and highlights the need to improve TDD because of its other benefits.The second part of the dissertation explores a method of applying mutation analysis to TDD to reduce the defect reduction gap between the two methods and to make TDD more reliable and predictable. A new change impact analysis algorithm (CHA-AS) based on CHA is presented and evaluated for applications of software change impact analysis where a predetermined set of program entry points is not available or is not known. An estimated average case complexity analysis indicates that the algorithm's time and space complexity is linear in the size of the program under analysis, and a simulation experiment indicates that the algorithm can capitalize on the iterative nature of TDD to produce a cost savings in mutation analysis applied to TDD projects. The algorithm should also be useful for other change impact analysis situations with undefined program entry points such as code library and framework development.An enhanced TDD method is proposed that incorporates mutation analysis, and a set of future research directions are proposed for developing tools to support mutation analysis enhanced TDD and to continue to improve the TDD method.
29
Yilmaz, Gokcen. "An Automated Defect Detection Approach For Cosmic Functional Size Measurement Method." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614646/index.pdf.
Повний текст джерелаАнотація:
Software size measurement provides a basis for software project management and plays an important role for its activities such as project management estimations, process benchmarking, and quality control. As size can be measured with functional size measurement (FSM) methods in the early phases of the software projects, functionality is one of the most frequently used metric. On the other hand, FSMs are being criticized by being subjective.
The main aim of this thesis is increasing the accuracy of the measurements, by decreasing the number of defects concerning FSMs that are measured by COSMIC FSM method. For this purpose, an approach that allows detecting defects of FSMs automatically is developed. During the development of the approach, first of all error classifications are established. To detect defects of COSMIC FSMs automatically, COSMIC FSM Defect Detection Approach (DDA) is proposed. Later, based on the proposed approach, COSMIC FSM DDT (DDT) is developed.
30
CHANG, RAY-YAUNG. "Discovering Neglected Conditions in Software by Mining Program Dependence Graphs." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1218722056.
Повний текст джерела
31
Sargut, Kamil Umut. "Application Of Statistical Process Control To Software Development Processes Via Control Charts." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1270081/index.pdf.
Повний текст джерелаАнотація:
The application of Statistical Process Control (SPC) to software processes has
been a challenging issue for software engineers and researchers. Although SPC is
suggested for providing process control and achieving higher process maturity
levels, there are very few resources that describe success stories, implementation
details, and implemented guidelines for applying SPC to specific metrics.
In this thesis the findings of a case study that is performed for investigating the
applicability of SPC to software metrics in an emergent CMM Level 3 software
organization are presented. As being one of the basic and most sophisticated tools
of SPC, control charts are used for the analysis. The difficulties in application of
Statistical Process Control to a CMM Level 3 organization are observed by using
the existing data of defect density, rework percentage, productivity and review
performance metrics and relevant suggestions are provided for dealing with them.
Finally the analysis results are summarized and a guideline is prepared for
software companies who want to utilize control charts by using their existing
metric data.
32
Bhatti, Khurram, and Ahmad Nauman Ghazi. "Effectiveness of Exploratory Testing, An empirical scrutiny of the challenges and factors affecting the defect detection efficiency." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5456.
Повний текст джерелаАнотація:
Context: Software testing is an integral part of software development life cycle. To improve the quality of software there are different testing approaches practiced over the years. Traditionally software testing is carried out by following approach focusing on prior test design. While exploratory testing is an approach to test software where the tester does not require to follow a specific test design. But rather, exploratory testing should facilitate the tester in testing the complete system comprehensively. Exploratory testing is seen by some, as a way to conduct simultaneous learning, test design and execution of tests simultaneously. While others point to exploratory testing enabling constant evolution of tests in an easy manner. Objectives: In this study we have investigated the field of exploratory testing in literature and industry to understand its perception and application. Further among the stated claims by practitioners, we selected defect detection efficiency and effectiveness claim for empirical validation through an experiment and survey. Methods: In this study, a systematic literature review, interview, experiment and survey are conducted. In the systematic review a number of article sources are used, including IEEE Xplore, ACM Digital Library, Engineering village, Springer Link, Google Scholar and Books database. The systematic review also includes the gray literature published by the practitioners. The selection of studies was done using two-phase and tollgate approach. A total of 47 references were selected as primary studies. Eight semi-structures interviews were conducted with industry practitioners. Experiment had total 4 iterations and 70 subjects. The subjects were selected from industry and academia. The experimental design used was one factor with two interventions and one response variable. Results: Based on our findings from literature review and interviews, the understanding of exploratory testing has improved over the period but still lacks empirical investigation. The results drawn from experimental and survey data shows that exploratory testing proved effective and efficient in finding more critical bugs in limited time. Conclusions: We conclude that exploratory testing has a lot of potential and much more to offer to testing industry. But more empirical investigation and true facts and figures are required to motivate the testing industry to adapt it. We have reported a number of advantages, disadvantages, challenges and factors in this study. We further investigated the claims stated by the ET practitioners through an experiment and survey. The statistical tests were conducted on the collected data to draw meaningful results. We found statistical significance difference in number of true defects found. Using exploratory testing approach testers found far more defects than test case based testing. Although, there was no statistical significance difference between the two approaches for false defects.Slutsatser: Vi anser att det experimentella tester har stor potential och mycket mer att erbjuda testning industrin. Men mer empirisk undersökning och sann fakta och siffror är skyldiga att motivera testning industrin att anpassa den. Vi har rapporterat en rad fördelar, nackdelar, utmaningar och faktorer i denna studie. Vi undersökte vidare fordringar anges av ET utövare genom ett experiment och undersökning. De statistiska test genomfördes på insamlade data för att dra meningsfulla resultat. Vi fann statistisk signifikans skillnaden i antalet sann fel som upptäcks. Använda utforskande testning strategi testare fann långt fler fel än testfall baserat testning. Även om det inte fanns någon statistisk signifikans skillnad mellan de två synsätten för falska defekter.
0046 73 651 8048
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Sivrioglu, Damla. "A Method For Product Defectiveness Prediction With Process Enactment Data In A Small Software Organization." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614516/index.pdf.
Повний текст джерелаАнотація:
As a part of the quality management, product defectiveness prediction is vital for small software
organizations as for instutional ones. Although for defect prediction there have been conducted a
lot of studies, process enactment data cannot be used because of the difficulty of collection.
Additionally, there is no proposed approach known in general for the analysis of process
enactment data in software engineering.
In this study, we developed a method to show the applicability of process enactment data for
defect prediction and answered &ldquoIs process enactment data beneficial for defect prediction?&rdquo
, &ldquo
How can we use process enactment data?&rdquo
and &ldquo
Which approaches and analysis methods can our method support?&rdquo
questions. We used multiple case study design and conducted case studies including with and without process enactment data in a small software development company. We preferred machine learning approaches rather than statistical ones, in order to cluster the data which includes process enactment informationsince we believed that they are convenient with the pattern oriented nature of the data. By the case studies performed, we obtained promising results. We evaluated performance values of prediction models to demonstrate the advantage of using process enactment data for the prediction of defect open duration value. When we have enough data points to apply machine learning methods and the data can be clusteredhomogeneously, we observed approximately 3% (ranging from -10% to %17) more accurate results from analyses including with process enactment data than the without ones. Keywords:
34
Kučera, Filip. "Softwarové ovládací prostředí pro měřicí metodu LBIC." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218413.
Повний текст джерелаАнотація:
This thesis is focused on one of the diagnostic methods of fotovoltaic cells the LBIC (Light Beam Induced Current) method and the experimental measurement set-up for this method, which is operated by the Department of Electrical and Electronic Technology in Brno. The principle of photovoltaic cells function, the possibly used material and the problems usually encountred during production are described in this work,. There is also a review of methods that can be used to detect defects in photovoltaic cells. The main part is devoted to the proposal of a new experimental set-up for the measuring LBIC method and software development for this set-up. There is a proposal of a new method of measurement, which is also implemented using the development environment Borland C + +. Newly developed software for this method allows simpler operation and more efficient measurements.
35
Руденко, Александр Антонович. "Вероятностные модели и методы оценивания надежности программных средств с учетом вторичных дефектов". Thesis, Полтавский национальный технический университет им. Ю. Кондратюка, 2015. http://repository.kpi.kharkov.ua/handle/KhPI-Press/19065.
Повний текст джерелаАнотація:
Диссертация на соискание ученой степени кандидата технических наук по специальности 05.13.06 – информационные технологии – Национальный технический университет "Харьковский политехнический институт", Харьков, 2015.
Диссертация посвящена разработке моделей, методов оценки надежности программно-технических комплексов, информационной технологии на основе
учета внесения вторичных дефектов. Как показывает проведенный анализ, необходимость обеспечения точности оценки надежности программного обеспечения обуславливает актуальность научных исследований, посвященных разработке и совершенствованию методов и моделей оценки. В существующих моделях оценки надежности не учитывается фактор вторичных дефектов или этому аспекту не уделяется внимание вообще. Это может привести, с одной стороны, к неэффективному применению и распределению методов и средств повышения надежности, а с другой, к недооценке рисков, связанных с возникновением отказов. Усовершенствованы вероятностные модели оценки надежности программных средств на основе учета параметров вторичных дефектов, путем модификаций функций риска этих моделей, что позволяет адекватно отображать процессы тестирования и сопровождения программных средств. В рамках исследования был проведен анализ классификаций моделей, анализ вероятностных моделей повышения надежности на предмет возможности их модификаций с тем, чтобы учитывать вторичные дефекты. Наиболее целесообразно в контексте поставленной задачи использовать модель Джелински-Моранды. Разработан метод оценивания числа вторичных дефектов программных средств, основанный на анализе статистических данных проявления первичных дефектов программных средств, что позволяет повысить точность количественных оценок эксплуатационных показателей. Потребность в разработке метода вызвана трудностями аналитического нахождения вторичных дефектов на основе моделей оценки надежности программных средств. В методе оценивания числа вторичных дефектов по статистическим данным выявления дефектов учитываются факторы раннего и поздних этапов тестирования (эксплуатации), что соответствует реалиям соответствующих этапов жизненного цикла программ.The dissertation on obtaining the scientific degree of candidate of technical sciences in the specialty 05.13.06 – information technologies – National technical University "Kharkiv Polytechnic Institute", Kharkov, 2015. The dissertation dedicated to the developing of models, methods of reliability estimation of software-technical complexes of information technology on the basis of making secondary defects. Scientific results are: improving probabilistic models of reliability estimation of software based on the parameters of secondary defects by modifying the risk function of these models that allows to reflect processes of testing and maintenance of software; method of estimating secondary defects of software tools that is based on the analysis of statistical data of manifestation of primary defects of software tools that allows to raise the accuracy of the quantitative assessment of performance indicators; the method of calculating the average intensity of manifestation of defects and the average change in the intensity of manifestation of defects with the help of modified model Jelinski-Moranda that, unlike existing, takes into account factor of secondary defects that allows to verify the reliability of software tools. Information technology of assessment the secure of software tools taking into account the secondary defects is devised basing on the method of estimating the number of secondary defects according to the statistics of defect detection and the method of calculating the average intensity of manifestation of defects and the average change in the intensity of manifestation of defects. The proposed models and methods allow to raise the accuracy of estimation of reliability of software and hardware complexes that is achieved by taking into account the factor of secondary defects.
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Руденко, Олександр Антонович. "Імовірнісні моделі та методи оцінювання надійності програмних засобів з урахуванням вторинних дефектів". Thesis, ТОВ "Фірма "Техсервіс", 2015. http://repository.kpi.kharkov.ua/handle/KhPI-Press/19064.
Повний текст джерелаАнотація:
Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.06 – інформаційні технології – Національний технічний університет "Харківський політехнічний інститут", Харків, 2015.
Дисертація присвячена розробці моделей, методів оцінювання надійності програмно-технічних комплексів, інформаційної технології на основі врахування внесення вторинних дефектів. Науковими результатами є: удосконалення імовірнісних моделей оцінки надійності програмних засобів на основі врахування параметрів вторинних дефектів шляхом модифікації функцій ризику цих моделей, що дозволяє адекватно відображати процеси тестування і супроводу програмних засобів; метод оцінювання числа вторинних дефектів програмних засобів, що ґрунтується на аналізі статистичних даних прояву первинних дефектів програмних засобів, що дозволяє підвищити точність оцінок кількісних експлуатаційних показників; метод обчислення середньої інтенсивності прояву дефектів і середньої зміни інтенсивності прояву дефектів за допомогою модифікованої моделі Джелінські-Моранди, у якому, на відміну від існуючих, враховується фактор вторинних дефектів, що дозволяє верифікувати показники надійності програмних засобів. На основі методу оцінювання числа вторинних дефектів за статистичними даними виявлення дефектів та методу обчислення середньої інтенсивності прояву дефектів і середньої зміни інтенсивності прояву дефектів розроблена інформаційна технологія оцінювання надійності програмних засобів з урахуванням вторинних дефектів. Запропоновані моделі і методи дозволяють підвищити точність оцінювання надійності програмно-технічних комплексів, що досягається за рахунок урахування фактора вторинних дефектів.The dissertation on obtaining the scientific degree of candidate of technical sciences in the specialty 05.13.06 – information technologies – National technical University "Kharkiv Polytechnic Institute", Kharkov, 2015. The dissertation dedicated to the developing of models, methods of reliability estimation of software-technical complexes of information technology on the basis of making secondary defects. Scientific results are: improving probabilistic models of reliability estimation of software based on the parameters of secondary defects by modifying the risk function of these models that allows to reflect processes of testing and maintenance of software; method of estimating secondary defects of software tools that is based on the analysis of statistical data of manifestation of primary defects of software tools that allows to raise the accuracy of the quantitative assessment of performance indicators; the method of calculating the average intensity of manifestation of defects and the average change in the intensity of manifestation of defects with the help of modified model Jelinski-Moranda that, unlike existing, takes into account factor of secondary defects that allows to verify the reliability of software tools. Information technology of assessment the secure of software tools taking into account the secondary defects is devised basing on the method of estimating the number of secondary defects according to the statistics of defect detection and the method of calculating the average intensity of manifestation of defects and the average change in the intensity of manifestation of defects. The proposed models and methods allow to raise the accuracy of estimation of reliability of software and hardware complexes that is achieved by taking into account the factor of secondary defects.
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Sundström, Alex. "Investigation into predicting unit test failure using syntactic source code features." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233382.
Повний текст джерелаАнотація:
In this thesis the application of software defect prediction to predict unit test failure is investigated. Data for this purpose was collected from a Continuous Integration development environment. Experiments were performed using semantic features from the source code. As the data was imbalanced with defective samples being in minority different degrees of oversampling were also evaluated. The data collection process revealed that even though several different code commits were available few ever failed a unit test. Difficulties with linking a failure to a specific file were also encountered. The machine learning model used in the project produced poor results when compared against related work, from which it was based on. In F-measure, it on average achieve 53% of the mean performance of state-of-the-art for software defect prediction on bugs in Java source files. Specifically, it would appear that very little information was available for the model to learn defects in files not present in training data.I denna avhandling undersöks applikationen av prognos för mjukvarudefekter för att förutse enhetstestfel. Data för detta syfte samlades in från en utvecklingsmiljö med kontinuerlig integration. Experimenten utfördes med användning av semantiska särdrag samlade från källkod. Då data var obalanserat med defekta exempel i minoritet evaluerades olika grader av översampling. Datainsamlingsprocessen visade att även om det fanns många kodinlämningar så misslyckades få någonsin ett enhetstest. Svårigheter med att länka testmisslyckanden till en specifik fil påträffades också. Den använda maskininlärningsmodellen uppvisade också dåliga resultat i jämförelse med relaterade värk. Mätt i F-measure uppnåddes i genomsnitt 53% av genomsnittlig prestandan av bästa möjliga prognos av mjukvarudefekter av buggar i Java källkod. Specifikt så framträdde det att väldigt lite information verkar finnas för modellen att lära sig defekter i filer som ej fanns med i träningsdata.
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Moriggl, Irene. "Intelligent Code Inspection using Static Code Features : An approach for Java." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4149.
Повний текст джерелаАнотація:
Effective defect detection is still a hot issue when it comes to software quality assurance. Static source code analysis plays thereby an important role, since it offers the possibility for automated defect detection in early stages of the development. As detecting defects can be seen as a classification problem, machine learning is recently investigated to be used for this purpose. This study presents a new model for automated defect detection by means of machine learn- ers based on static Java code features. The model comprises the extraction of necessary features as well as the application of suitable classifiers to them. It is realized by a prototype for the feature extraction and a study on the prototype’s output in order to identify the most suitable classifiers. Finally, the overall approach is evaluated in a using an open source project. The suitability study and the evaluation show, that several classifiers are suitable for the model and that the Rotation Forest, Multilayer Perceptron and the JRip classifier make the approach most effective. They detect defects with an accuracy higher than 96%. Although the approach comprises only a prototype, it shows the potential to become an effective alternative to nowa- days defect detection methods.
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CAVALCANTI, Diego Tavares. "Estudo do uso de vocabulários para analisar o impacto de relatórios de defeitos a código-fonte." Universidade Federal de Campina Grande, 2012. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1839.
Повний текст джерелаАнотація:
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Localizar e corrigir defeitos são tarefas comuns no processo de manutenção de software. Entretanto, a atividade de localizar entidades de código que são possivelmente defeituosas e que necessitam ser modificadas para a correção de um defeito, não é trivial. Geralmente, desenvolvedores realizam esta tarefa por meio de um processo manual de leitura e inspeção do código, bem como de informações cadastradas em relatórios de defeitos. De fato, é necessário que os desenvolvedores tenham um bom conhecimento da arquitetura e do design do software a fim de realizarem tal tarefa. Entretanto, este conhecimento fica espalhado por entre a equipe e requer tempo para ser adquirido por novatos. Assim, é necessário o desenvolvimento de técnicas que auxiliem na tarefa de análise de impacto de relatórios de defeitos no código, independente da experiência do desenvolvedor que irá executá-la. Neste trabalho, apresentamos resultados de um estudo empírico no qual avaliamos se a análise automática de vocabulários de relatórios de defeitos e de software pode ser útil na tarefa de localizar defeitos no código. Nele, analisamos similaridade de vocabulários como fator para sugerir classes que são prováveis de serem impactadas por um dado relatório de defeito. Realizamos uma avaliação com oito projetos maduros de código aberto, desenvolvidos em Java, que utilizam Bugzilla e JIRA como seus repositórios de defeitos. Nossos resultados indicam que a análise de ambos os vocabulários é, de fato, uma fonte valiosa de informação, que pode ser utilizada para agilizar a tarefa de localização de defeitos. Para todos os sistemas estudados, ao considerarmos apenas análise de vocabulário, vimos que, mesmo com um ranking contendo apenas 8% das classes de um projeto, foi possível encontrar classes relacionadas ao defeito buscado em até 75% dos casos. Portanto, podemos concluir que, mesmo que não possamos utilizar vocabulários de software e de relatórios de defeitos como únicas fontes de informação, eles certamente podem melhorar os resultados obtidos, ao serem combinados com técnicas complementares.
Locating and fixing bugs described in bug reports are routine tasks in software development processes. A major effort must be undertaken to successfully locate the (possibly faulty) entities in the code that must be worked on. Generally, developers map bug reports to code through manual reading and inspection of both bug reports and the code itself. In practice, they must rely on their knowledge about the software architecture and design to perform the mapping in an efficient and effective way. However, it is well known that architectural and design knowledge is spread out among developers. Hence, the success of such a task is directly depending on choosing the right developer. In this paper, we present results of an empirical study we performed to evaluate whether the automated analysis of bug reports and software vocabularies can be helpful in the task of locating bugs. We conducted our study on eight versions of six mature Java open-source projects that use Bugzilla and JIRA as bug tracking systems. In our study, we have used Information Retrieval techniques to assess the similarity of bug reports and code entities vocabularies. For each bug report, we ranked ali code entities according to the measured similarity. Our results indicate that vocabularies are indeed a valuable source of information that can be used to narrow down the bug-locating task. For ali the studied systems, considering vocabulary similarity only, a Top 8% list of entities has about 75% of the target entities. We conclude that while vocabularies cannot be the sole source of information, they can certainly improve results if combined with other techniques.
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OLIVEIRA, Paulo César de. "Abordagem semi-supervisionada para detecção de módulos de software defeituosos." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/19990.
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Com a competitividade cada vez maior do mercado, aplicações de alto nível de qualidade são exigidas para a automação de um serviço. Para garantir qualidade de um software, testá-lo visando encontrar falhas antecipadamente é essencial no ciclo de vida de desenvolvimento. O objetivo do teste de software é encontrar falhas que poderão ser corrigidas e consequentemente, aumentar a qualidade do software em desenvolvimento. À medida que o software cresce, uma quantidade maior de testes é necessária para prevenir ou encontrar defeitos, visando o aumento da qualidade. Porém, quanto mais testes são criados e executados, mais recursos humanos e de infraestrutura são necessários. Além disso, o tempo para realizar as atividades de teste geralmente não é suficiente, fazendo com que os defeitos possam escapar. Cada vez mais as empresas buscam maneiras mais baratas e efetivas para detectar defeitos em software. Muitos pesquisadores têm buscado nos últimos anos, mecanismos para prever automaticamente defeitos em software. Técnicas de aprendizagem de máquina vêm sendo alvo das pesquisas, como uma forma de encontrar defeitos em módulos de software. Tem-se utilizado muitas abordagens supervisionadas para este fim, porém, rotular módulos de software como defeituosos ou não para fins de treinamento de um classificador é uma atividade muito custosa e que pode inviabilizar a utilização de aprendizagem de máquina. Neste contexto, este trabalho propõe analisar e comparar abordagens não supervisionadas e semisupervisionadas para detectar módulos de software defeituosos. Para isto, foram utilizados métodos não supervisionados (de detecção de anomalias) e também métodos semi-supervisionados, tendo como base os classificadores AutoMLP e Naive Bayes. Para avaliar e comparar tais métodos, foram utilizadas bases de dados da NASA disponíveis no PROMISE Software Engineering Repository.
Because the increase of market competition then high level of quality applications are required to provide automate services. In order to achieve software quality testing is essential in the development lifecycle with the purpose of finding defect as earlier as possible. The testing purpose is not only to find failures that can be fixed, but improve software correctness and quality. Once software gets more complex, a greater number of tests will be necessary to prevent or find defects. Therefore, the more tests are designed and exercised, the more human and infrastructure resources are needed. However, time to run the testing activities are not enough, thus, as a result, it causes escape defects. Companies are constantly trying to find cheaper and effective ways to software defect detection in earlier stages. In the past years, many researchers are trying to finding mechanisms to automatically predict these software defects. Machine learning techniques are being a research target, as a way of finding software modules detection. Many supervised approaches are being used with this purpose, but labeling software modules as defective or not defective to be used in training phase is very expensive and it can make difficult machine learning use. Considering that this work aims to analyze and compare unsupervised and semi-supervised approaches to software module defect detection. To do so, unsupervised methods (of anomaly detection) and semi-supervised methods using AutoMLP and Naive Bayes algorithms were used. To evaluate and compare these approaches, NASA datasets were used at PROMISE Software Engineering Repository.
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Marín, Campusano Beatriz Mariela. "Functional Size Measurement and Model Verification for Software Model-Driven Developments: A COSMIC-based Approach." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/11237.
Повний текст джерелаАнотація:
Historically, software production methods and tools have a unique goal: to produce high quality
software. Since the goal of Model-Driven Development (MDD) methods is no different, MDD
methods have emerged to take advantage of the benefits of using conceptual models to produce
high quality software.
In such MDD contexts, conceptual models are used as input to automatically generate final
applications. Thus, we advocate that there is a relation between the quality of the final software
product and the quality of the models used to generate it. The quality of conceptual models can
be influenced by many factors. In this thesis, we focus on the accuracy of the techniques used to
predict the characteristics of the development process and the generated products.
In terms of the prediction techniques for software development processes, it is widely
accepted that knowing the functional size of applications in order to successfully apply effort
models and budget models is essential. In order to evaluate the quality of generated
applications, defect detection is considered to be the most suitable technique.
The research goal of this thesis is to provide an accurate measurement procedure based on
COSMIC for the automatic sizing of object-oriented OO-Method MDD applications. To
achieve this research goal, it is necessary to accurately measure the conceptual models used in
the generation of object-oriented applications. It is also very important for these models not to
have defects so that the applications to be measured are correctly represented.
In this thesis, we present the OOmCFP (OO-Method COSMIC Function Points) measurement
procedure. This procedure makes a twofold contribution: the accurate measurement of objectoriented
applications generated in MDD environments from the conceptual models involved, and
the verification of conceptual models to allow the complete generation of correct final applications
from the conceptual models involved.
The OOmCFP procedure has been systematically designed, applied, and
automated. This measurement procedure has been validated to conform to the
ISO 14143 standard, the metrology concepts defined in the ISO VIM, and the
accuracy of the measurements obtained according to ISO 5725. This
procedure has also been validated by performing empirical studies.
The results of the empirical studies demonstrate that OOmCFP can obtain
accurate measures of the functional size of applications generated in MDD
environments from the corresponding conceptual models.Marín Campusano, BM. (2011). Functional Size Measurement and Model Verification for Software Model-Driven Developments: A COSMIC-based Approach [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11237
Palancia
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Mahmood, Waqas, and Muhammad Faheem Akhtar. "Validation of Machine Learning and Visualization based Static Code Analysis Technique." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4347.
Повний текст джерелаАнотація:
Software security has always been an afterthought in software development which results into insecure software. Companies rely on penetration testing for detecting security vulnerabilities in their software. However, incorporating security at early stage of development reduces cost and overhead. Static code analysis can be applied at implementation phase of software development life cycle. Applying machine learning and visualization for static code analysis is a novel idea. Technique can learn patterns by normalized compression distance NCD and classify source code into correct or faulty usage on the basis of training instances. Visualization also helps to classify code fragments according to their associated colors. A prototype was developed to implement this technique called Code Distance Visualizer CDV. In order test the efficiency of this technique empirical validation is required. In this research we conduct series of experiments to test its efficiency. We use real life open source software as our test subjects. We also collected bugs from their corresponding bug reporting repositories as well as faulty and correct version of source code. We train CDV by marking correct and faulty version of code fragments. On the basis of these trainings CDV classifies other code fragments as correct or faulty. We measured its fault detection ratio, false negative and false positive ratio. The outcome shows that this technique is efficient in defect detection and has low number of false alarms.Software trygghet har alltid varit en i efterhand inom mjukvaruutveckling som leder till osäker mjukvara. Företagen är beroende av penetrationstester för att upptäcka säkerhetsproblem i deras programvara. Att införliva säkerheten vid tidigt utvecklingsskede minskar kostnaderna och overhead. Statisk kod analys kan tillämpas vid genomförandet av mjukvaruutveckling livscykel. Tillämpa maskininlärning och visualisering för statisk kod är en ny idé. Teknik kan lära mönster av normaliserade kompressionständning avstånd NCD och klassificera källkoden till rätta eller felaktig användning på grundval av utbildning fall. Visualisering bidrar också till att klassificera code fragment utifrån deras associerade färger. En prototyp har utvecklats för att genomföra denna teknik som kallas Code Avstånd VISUALISERARE CDV. För att testa effektiviteten hos denna teknik empirisk validering krävs. I denna forskning vi bedriver serie experiment för att testa dess effektivitet. Vi använder verkliga livet öppen källkod som vår test ämnen. Vi har också samlats in fel från deras motsvarande felrapportering förråd samt fel och rätt version av källkoden. Vi utbildar CDV genom att markera rätt och fel version av koden fragment. På grundval av dessa träningar CDV klassificerar andra nummer fragment som korrekta eller felaktiga. Vi mätt sina fel upptäckt förhållandet falska negativa och falska positiva förhållandet. Resultatet visar att den här tekniken är effektiv i fel upptäckt och har låga antalet falsklarm.
waqasmah@gmail.com +46762316108
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Ahmed, Israr, and Shahid Nadeem. "Minimizing Defects Originating from Elicitation, Analysis and Negotiation (E and A&N) Phase in Bespoke Requirements Engineering." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4070.
Повний текст джерелаАнотація:
Defect prevention (DP) in early stages of software development life cycle (SDLC) is very cost effective than in later stages. The requirements elicitation and analysis & negotiation (E and A&N) phases in requirements engineering (RE) process are very critical and are major source of requirements defects. A poor E and A&N process may lead to a software requirements specifications (SRS) full of defects like missing, ambiguous, inconsistent, misunderstood, and incomplete requirements. If these defects are identified and fixed in later stages of SDLC then they could cause major rework by spending extra cost and effort. Organizations are spending about half of their total project budget on avoidable rework and majority of defects originate from RE activities. This study is an attempt to prevent requirements level defects from penetrates into later stages of SDLC. For this purpose empirical and literature studies are presented in this thesis. The empirical study is carried out with the help of six companies from Pakistan & Sweden by conducting interviews and literature study is done by using literature reviews. This study explores the most common requirements defect types, their reasons, severity level of defects (i.e. major or minor), DP techniques (DPTs) & methods, defect identification techniques that have been using in software development industry and problems in these DPTs. This study also describes possible major differences between Swedish and Pakistani software companies in terms of defect types and rate of defects originating from E and A&N phases. On the bases of study results, some solutions have been proposed to prevent requirements defects during the RE process. In this way we can minimize defects originating from E and A&N phases of RE in the bespoke requirements engineering (BESRE).
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PERES, Glaucia Boudox. "A black-box testing technique for the detection of crashes based on automated test scenarios." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/2366.
Повний текст джерелаАнотація:
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Previous issue date: 2009Boudox Peres, Glaucia; Cabral Mota, Alexandre. A black-box testing technique for the detection of crashes based on automated test scenarios. 2009. Dissertação (Mestrado). Programa de Pós-Graduação em Ciência da Computação, Universidade Federal de Pernambuco, Recife, 2009.
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Řezníček, Martin. "Inovace měřicího pracoviště pro měření solárních článků." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217979.
Повний текст джерелаАнотація:
The master‘s thesis is focused on the modification of measurement workplace for solar cells. In the first part of the thesis are introduced general problems of the solar energy and possible use in the international power supply, the details about the photovoltaic effect, processing of solar cells and their subsequent parameters. More further in the text the autor is concerned with causes of solar cells defects formation and representation of the most important defects. For defects detection are known the detection methods of solar cells, which are generally described in the text. The second part of the thesis includes detailed description of the LBIC method and workplace both the VUT Brno, and in Solartec, Ltd. The most important point of this part is project and description of innovated workplace from hardware and software realization. There is out of print the function and the description of the user interface and subsequently there are mentioned results gained from original and innovated workplace. In the conclusion are summarized whole activities of the master’s thesis, described and evaluated achieved results and outlined the direction, where would the other development of the LBIC workplace on VUT Brno be proceeding.
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Faustová, Tereza. "Nástroje na podporu testování." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-11762.
Повний текст джерелаАнотація:
The topic of this thesis is the issue of software testing. The thesis places main emphasis on tools to support test management, manual and automated functional testing and last but not least the tools for defect tracking. The aim of this thesis is introduce readers with software testing, especially with tools that can be used to support testing. The aim is offer an overview of the basic commercial and freely distributed tools for test management, manual testing, automated functional testing and defect tracking. Another aim is design criteria that simplify selection of tool. The second aim of this thesis is practical example of the configuration and description of the basic work with tools IBM Rational - ClearQuest, ClearCase, Manual Tester and Functional Tester. The aims of this thesis were achieved by studying available sources and by own practical experience with the tools to support testing. The contribution of this thesis lies in the characteristics of the selected tools to support testing and especially in design of criteria by which tools can be selected. The last part of thesis provides practical instruction how to configure and work with the tools to support testing of IBM Rational. The thesis is conceived in three main parts. The first part attends to basic terms, which can be found in the area of testing, and to overview of types of tests. There are also described two most famous life-cycle models and methodology RUP. The second part attends to overview of tools to support testing, attention is given to areas: test management, manual testing, defect tracking, and automated functional testing. For each category of tools has been defined criteria according to which tools can be selected. The last part attends to practical example of setting and basic work with the selected tools to support testing of IBM Rational.
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Wang, Hui. "Software Defects Classification Prediction Based On Mining Software Repository." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-216554.
Повний текст джерелаАнотація:
An important goal during the cycle of software development is to find and fix existing defects as early as possible. This has much to do with software defects prediction and management. Nowadays,many big software development companies have their own development repository, which typically includes a version control system and a bug tracking system. This has no doubt proved useful for software defects prediction. Since the 1990s researchers have been mining software repository to get a deeper understanding of the data. As a result they have come up with some software defects prediction models the past few years. There are basically two categories among these prediction models. One category is to predict how many defects still exist according to the already captured defects data in the earlier stage of the software life-cycle. The other category is to predict how many defects there will be in the newer version software according to the earlier version of the software defects data. The complexities of software development bring a lot of issues which are related with software defects. We have to consider these issues as much as possible to get precise prediction results, which makes the modeling more complex. This thesis presents the current research status on software defects classification prediction and the key techniques in this area, including: software metrics, classifiers, data pre-processing and the evaluation of the prediction results. We then propose a way to predict software defects classification based on mining software repository. A way to collect all the defects during the development of software from the Eclipse version control systems and map these defects with the defects information containing in software defects tracking system to get the statistical information of software defects, is described. Then the Eclipse metrics plug-in is used to get the software metrics of files and packages which contain defects. After analyzing and preprocessing the dataset, the tool(R) is used to build a prediction models on the training dataset, in order to predict software defects classification on different levels on the testing dataset, evaluate the performance of the model and comparedifferent models’ performance.
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Nakamura, Taiga. "Recurring software defects in high end computing." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/7217.
Повний текст джерелаАнотація:
Thesis (Ph. D.) -- University of Maryland, College Park, 2007.Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Hickman, Björn, and Victor Holmqvist. "Predict future software defects through machine learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301864.
Повний текст джерелаАнотація:
The thesis aims to investigate the implications of software defect predictions through machine learning on project management. In addition, the study aims to examine what features of a code base that are useful for making such predictions. The features examined are of both organisational and technical nature, indicated to correlate with the introductions of software defects by previous studies. The machine learning algorithms used in the study are Random forest, logistic regression and naive Bayes. The data was collected from an open source git-repository, VSCode, where the correct classifications of reported defects originated from GitHub-Issues. The results of the study indicate that both technical features of a code base, as well as organisational factors can be useful when predicting future software defects. All three algorithms showed similar performance. Furthermore, the ML-models presented in this study show some promise as a complementary tool in project management decision making, more specifically decisions regarding planning, risk assessment and resource allocation. However, further studies in this area are of interest, in order to confirm the findings of this study and it’s limitations.Rapportens mål var att undersöka potentiella effekter av att predicera mjukvarudefekter i ett mjukvaruprojekt. Detta genomfördes med hjälp av maskininlärning. Vidare undersöker studien vilka särdrag hos en kodbas som är av intresse för att genomföra dessa prediktioner. De undersökta särdrag som användes för att träna modellerna var av både teknisk såväl som organisatorisk karaktär. Modellerna som användes var Random forest, logistisk regression och naive Bayes. Data hämtades från ett open source git-repository, VSCode, där korrekta klassificeringar av rapporterade defekter hämtades från GitHub-Issues. Rapportens resultat ger indikationer på att både tekniska och organisatoriska särdrag är av relevans. Samtliga tre modeller påvisade liknande resultat. Vidare kan modellernas resultat visa stöd för att användas som ett komplementärt verktyg vid projektledning av mjukvaruprojekt. Närmare bestämt stöd vid riskplanering, riskbedömning och vid resursallokering. Vidare skulle fortsatta studier inom detta område vara av intresse för att bekräfta denna studies slutsatser.
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Couto, César Francisco de Moura. "Predicting software defects with causality tests = Predizendo defeitos de software com testes de causalidade." Universidade Federal de Minas Gerais, 2013. http://hdl.handle.net/1843/ESBF-9GMMLN.
Повний текст джерелаАнотація:
Defect prediction is a central area of research in software engineering that aims to identify the components of a software system that are more likely to present defects. Despite the large investment in research aiming to identify an effective way to predict defects in software systems, there is still no widely used solution to this problem. Current defect prediction approaches present at least two main problems in the current defect prediction approaches. First, most approaches do not consider the idea of causality between software metrics and defects. More specifically, the studies performed to evaluate defect prediction techniques do not investigate in-depth whether the discovered relationships indicate cause-effect relations or whether they are statistical coincidences. The second problem concerns the output of the current defect prediction models. Typically, most indicate the number or the existence of defects in a component in the future. Clearly, the availability of this information is important to foster software quality. However, predicting defects as soon as they are introduced in the code is more useful to maintainers than simply signaling the future occurrences of defects.
To tackle these questions, in this thesis we propose a defect prediction approach centered on more robust evidences towards causality between source code metrics (as predictors) and the occurrence of defects. More specifically, we rely on a statistical hypothesis test proposed by Clive Granger to evaluate whether past variations in source code metrics values can be used to forecast changes in time series of defects. The Granger Causality Test was originally proposed to evaluate causality between time series of economic data. Our approach triggers alarms whenever changes made to the source code of a target system are likely to present defects. We evaluated our approach in several life stages of four Java-based systems. We reached an average precision greater than 50% in three out of the four systems we evaluated. Moreover, by comparing our approach with baselines that are not based on causality tests, it achieved a better precision.Predição de defeitos é uma área de pesquisa em engenharia de software que objetiva identificar os componentes de um sistema de software que são mais prováveis de apresentar defeitos. Apesar do grande investimento em pesquisa objetivando identificar uma maneira efetiva para predizer defeitos em sistemas de software, ainda não existe uma solução amplamente utilizada para este problema. As atuais abordagens para predição de defeitos apresentam pelo menos dois problemas principais. Primeiro, a maioria das abordagens não considera a idéia de causalidade entre métricas de software e defeitos. Mais especificamente, os estudos realizados para avaliar as técnicas de predição de defeitos não investigam em profundidade se as relações descobertas indicam relações de causa e efeito ou se são coincidências estatísticas. O segundo problema diz respeito a saída dos atuais modelos de predição de defeitos. Tipicamente, a maioria dos modelos indica o número ou a existência de defeitos em um componente no futuro. Claramente, a disponibilidade desta informação é importante para promover a qualidade de software. Entretanto, predizer defeitos logo que eles são introduzidos no código é mais útil para mantenedores que simplesmente sinalizar futuras ocorrências de defeitos. Para resolver estas questões, nós propomos uma abordagem para predição de defeitos centrada em evidências mais robustas no sentido de causalidade entre métricas de código fonte (como preditor) e a ocorrência de defeitos. Mais especificamente, nós usamos um teste de hipótese estatístico proposto por Clive Granger (Teste de Causalidade de Granger) para avaliar se variações passadas nos valores de métricas de código fonte podem ser usados para predizer mudanças em séries temporais de defeitos. Nossa abordagem ativa alarmes quando mudanças realizadas no código fonte de um sistema alvo são prováveis de produzir defeitos. Nós avaliamos nossa abordagem em várias fases da vida de quatro sistemas implementados em Java. Nós alcançamos um precisão média maior do que 50% em três dos quatro sistemas avaliados. Além disso, ao comparar nossa abordagem com abordagens que não são baseadas em testes de causalidade, nossa abordagem alcançou uma precisão melhor.