Готові списки джерел за темами / Software defect

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Добірка наукової літератури з теми "Software defect"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Software defect"

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Kumaresh, Sakthi, and R. Baskaran. "Software Defect Prevention through Orthogonal Defect Classification (ODC)." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (October 15, 2013): 2393–400. http://dx.doi.org/10.24297/ijct.v11i3.1166.

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Анотація:
“Quality is never an accident; it is always the result of intelligent effort” [10]. In the process of making quality software product, it is necessary to have effective defect prevention process, which will minimize the risk of making defects /errors in software deliverables. An ideal approach would involve effective software development process with an integrated defect prevention process. This paper presents a Defect Prevention Model in which Defect Prevention Process(DPP) is integrated into software development life cycle to reduce the defects at early stages itself, thereby reducing the defect arrival rate as the project progresses to the subsequent stages. Orthogonal Defect Classification (ODC) scheme involving defect trigger, defect type etc. are discussed in this work to illustrate how ODC can be used in the defect prevention process. ODC can be used to measure development progress with respect to product quality and identify process problems, which will help to come out with “Best Practices” to be followed to eradicate the defects in the subsequent projects.
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Kumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (March 23, 2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.

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Анотація:
Early detection of software defects is very important to decrease the software cost and subsequently increase the software quality. Success of software industries not only depends on gaining knowledge about software defects, but largely reflects from the manner in which information about defect is collected and used. In software industries, individuals at different levels from customers to engineers apply diverse mechanisms to detect the allocation of defects to a particular class. Categorizing bugs based on their characteristics helps the Software Development team take appropriate actions to reduce similar defects that might get reported in future releases. Classification, if performed manually, will consume more time and effort. Human resource having expert testing skills & domain knowledge will be required for labeling the data. Therefore, the need of automatic classification of software defect is high.This work attempts to categorize defects by proposing an algorithm called Software Defect CLustering (SDCL). It aims at mining the existing online bug repositories like Eclipse, Bugzilla and JIRA for analyzing the defect description and its categorization. The proposed algorithm is designed by using text clustering and works with three major modules to find out the class to which the defect should be assigned. Software bug repositories hold software defect data with attributes like defect description, status, defect open and close date. Defect extraction module extracts the defect description from various bug repositories and converts it into unified format for further processing. Unnecessary and irrelevant texts are removed from defect data using data preprocessing module. Finally grouping of defect data into clusters of similar defect is done using clustering technique. The algorithm provides classification accuracy more than 80% in all of the three above mentioned repositories.
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3

Malhotra, Ruchika, and Juhi Jain. "Predicting Software Defects for Object-Oriented Software Using Search-based Techniques." International Journal of Software Engineering and Knowledge Engineering 31, no. 02 (February 2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.

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Анотація:
Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build defect prediction models for 13 OO software projects. Stable performance measures — GMean, Balance and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) are employed to probe into the predictive capability of developed models, taking into consideration the imbalanced nature of software datasets. Proper measures are taken to handle the stochastic behavior of SBTs. The significance of results is statistically validated using the Friedman test complied with Wilcoxon post hoc analysis. The results confirm that software defects can be detected in the early phases of software development with help of SBTs. This paper identifies the effective subset of SBTs that will aid software practitioners to timely detect the probable software defects, therefore, saving resources and bringing up good quality softwares. Eight SBTs — sUpervised Classification System (UCS), Bioinformatics-oriented hierarchical evolutionary learning (BIOHEL), CHC, Genetic Algorithm-based Classifier System with Adaptive Discretization Intervals (GA_ADI), Genetic Algorithm-based Classifier System with Intervalar Rule (GA_INT), Memetic Pittsburgh Learning Classifier System (MPLCS), Population-Based Incremental Learning (PBIL) and Steady-State Genetic Algorithm for Instance Selection (SGA) are found to be statistically good defect predictors.
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Han, Wan Jiang, He Yang Jiang, Yi Sun, and Tian Bo Lu. "Software Defect Distribution Prediction for BOSS System." Applied Mechanics and Materials 701-702 (December 2014): 67–70. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.67.

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Анотація:
Effective detection of software defects is an important activity of software development process. In this paper, we propose an approach to predict residual defects for BOSS project, which applies defect distribution model. Experiment results show that this approach can effectively improve the accuracy of defect prediction.
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WANG, Qing. "Software Defect Prediction." Journal of Software 19, no. 7 (October 21, 2008): 1565–80. http://dx.doi.org/10.3724/sp.j.1001.2008.01565.

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Zhang, Wei, Zhen Yu Ma, Wen Ge Zhang, Qing Ling Lu, and Xiao Bing Nie. "Correlation Analysis of Software Defects Density and Metrics." Applied Mechanics and Materials 713-715 (January 2015): 2225–28. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2225.

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Анотація:
It is very useful for improving software quality if we can find which software metrics are more correlative with software defects or defects density. Based on 33 actual software projects, we analyzed 44 software metrics from application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defects, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density, these metrics can be used for improving software quality and predicting software defects density.
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CHANG, CHING-PAO. "INTEGRATING ACTION-BASED DEFECT PREDICTION TO PROVIDE RECOMMENDATIONS FOR DEFECT ACTION CORRECTION." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (March 2013): 147–72. http://dx.doi.org/10.1142/s0218194013500022.

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Анотація:
Reducing software defects is an essential activity for Software Process Improvement. The Action-Based Defect Prediction (ABDP) approach fragments the software process into actions, and builds software defect prediction models using data collected from the execution of actions and reported defects. Though the ABDP approach can be applied to predict possible defects in subsequent actions, the efficiency of corrections is dependent on the skill and knowledge of the stakeholders. To address this problem, this study proposes the Action Correction Recommendation (ACR) model to provide recommendations for action correction, using the Negative Association Rule mining technique. In addition to applying the association rule mining technique to build a High Defect Prediction Model (HDPM) to identify high defect action, the ACR builds a Low Defect Prediction Model (LDPM). For a submitted action, each HDPM rule used to predict the action as a high defect action and the LDPM rules are analyzed using negative association rule mining to spot the rule items with different characteristics in HDPM and LDPM rules. This information not only identifies the attributes required for corrections, but also provides a range (or a value) to facilitate the high defect action corrections. This study applies the ACR approach to a business software project to validate the efficiency of the proposed approach. The results show that the recommendations obtained can be applied to decrease software defect removal efforts.
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LIU, Hai, and Ke-gang HAO. "Defining software defect data." Journal of Computer Applications 28, no. 1 (October 14, 2008): 226–28. http://dx.doi.org/10.3724/sp.j.1087.2008.00226.

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Jones, C. "Software defect-removal efficiency." Computer 29, no. 4 (April 1996): 94–95. http://dx.doi.org/10.1109/2.488361.

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Hall, Robert J. "Editorial: software defect detection." Automated Software Engineering 17, no. 3 (May 26, 2010): 213–15. http://dx.doi.org/10.1007/s10515-010-0071-y.

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Дисертації з теми "Software defect"

1

Ye, Xin. "Automated Software Defect Localization." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1462374079.

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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.

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Анотація:
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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3

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.

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Анотація:
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/.

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Анотація:
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.
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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.

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Анотація:
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.

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Анотація:
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.

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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.

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Анотація:
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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Книги з теми "Software defect"

1

Zero defect software. New York: McGraw-Hill, 1990.

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2

Toward zero-defect programming. Reading, Mass: Addison-Wesley, 1999.

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3

Younessi, Houman. Object-oriented defect management of software. Upper Saddle River, NJ: Prentice Hall PTR, 2002.

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4

Cai, Kai-Yuan. Software Defect and Operational Profile Modeling. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5593-3.

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5

Software defect and operational profile modeling. Boston: Kluwer Academic Publishers, 1998.

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6

Miller, Ann K. Engineering quality software: Defect detection and prevention. Reading, Mass: Addison-Wesley, 1992.

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7

Peterson, Ivars. Fatal Defect: Chasing Killer Computer Bugs. New York: Vantage Books, 1996.

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8

Peterson, Ivars. Fatal Defect: Chasing Killer Computer Bugs. New York: Times Books, 1995.

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9

Huizinga, Dorota. Automated defect prevention: Best practices in software management. Hoboken, N.J: Wiley, 2007.

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10

Ferdinand, Arthur E. Systems, software, and qualityengineering: Applying defect behavior theory to programming. New York: Van Nostrand Reinhold, 1993.

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Частини книг з теми "Software defect"

1

Sorensen, Ib, and David Neilson. "B: Towards Zero Defect Software." In The Kluwer International Series in Engineering and Computer Science, 23–42. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1391-9_2.

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2

Ghosh, Soumi, Ajay Rana, and Vineet Kansal. "Predicting Defect of Software System." In Advances in Intelligent Systems and Computing, 55–67. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3156-4_6.

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3

Rodríguez, Daniel, R. Ruiz, J. C. Riquelme, and Rachel Harrison. "Subgroup Discovery for Defect Prediction." In Search Based Software Engineering, 269–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23716-4_25.

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4

Basili, Victor, and Forrest Shull. "Evolving Defect “Folklore”: A Cross-Study Analysis of Software Defect Behavior." In Unifying the Software Process Spectrum, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11608035_1.

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5

Turhan, Burak, Ayse Bener, and Tim Menzies. "Regularities in Learning Defect Predictors." In Product-Focused Software Process Improvement, 116–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13792-1_11.

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6

Evanco, William M. "Poisson Models for Subprogram Defect Analyses." In Achieving Quality in Software, 161–74. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34869-8_14.

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7

Jones, Capers. "Optimizing Software Defect Removal Efficiency (DRE)." In Software Development Patterns and Antipatterns, 309–26. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003193128-13.

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8

Ostrand, Thomas J., and Elaine J. Weyuker. "Progress in Automated Software Defect Prediction." In Hardware and Software: Verification and Testing, 200–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01702-5_20.

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9

Cai, Kai-Yuan. "Software Defect Estimations Under Imperfect Debugging." In The Kluwer International Series in Software Engineering, 163–86. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5593-3_7.

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10

Cai, Kai-Yuan. "Modeling of Probably Zero-Defect Software." In The Kluwer International Series in Software Engineering, 235–63. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5593-3_9.

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Тези доповідей конференцій з теми "Software defect"

1

Benson, Markland J. "Toward Intelligent Software Defect Detection - Learning Software Defects by Example." In 2011 34th Annual IEEE Software Engineering Workshop (SEW). IEEE, 2011. http://dx.doi.org/10.1109/sew.2011.26.

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2

Gokhale, Swapna S., and Robert Mullen. "Software defect repair times." In the 4th international workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1370788.1370810.

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3

Yusop, Nor Shahida Mohamad. "Understanding Usability Defect Reporting in Software Defect Repositories." In ASWEC ' 15 Vol. II: ASWEC 2015 24th Australasian Software Engineering Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2811681.2817757.

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4

"Defect Bash - Literature Review." In 8th International Conference on Evaluation of Novel Software Approaches to Software Engineering. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004417101250131.

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5

Singh, Pradeep. "Learning from Software defect datasets." In 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2019. http://dx.doi.org/10.1109/ispcc48220.2019.8988366.

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6

Gao, Junting, Liping Zhang, Fengrong Zhao, and Ye Zhai. "Research on Software Defect Classification." In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2019. http://dx.doi.org/10.1109/itnec.2019.8729440.

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7

Zhang, Qihang, and Bin Wu. "Software Defect Prediction via Transformer." In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2020. http://dx.doi.org/10.1109/itnec48623.2020.9084745.

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8

Oral, Atac Deniz, and Ayse Basar Bener. "Defect prediction for embedded software." In 2007 22nd international symposium on computer and information sciences. IEEE, 2007. http://dx.doi.org/10.1109/iscis.2007.4456886.

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9

Mockus, Audris. "Defect prediction and software risk." In PROMISE '14: The 10th International Conference on Predictive Models in Software Engineering. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2639490.2639511.

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10

Tosun, Ayse, Burak Turhan, and Ayse Bener. "Ensemble of software defect predictors." In the Second ACM-IEEE international symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1414004.1414066.

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Звіти організацій з теми "Software defect"

1

Thomas, R. Edward. Hardwood log defect photographic database, software and user's guide. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 2009. http://dx.doi.org/10.2737/nrs-gtr-40.

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2

Snijders, J., C. Morrow, and R. van Mook. Software Defects Considered Harmful. RFC Editor, April 2022. http://dx.doi.org/10.17487/rfc9225.

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3

Florac, William A. Software Quality Measurement: A Framework for Counting Problems and Defects. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada258556.

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