Gotowe bibliografie tematyczne / Software defects

Gotowa bibliografia na temat „Software defects”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 20 lutego 2023

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Artykuły w czasopismach na temat "Software defects"

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Malhotra, Ruchika, i Juhi Jain. "Predicting Software Defects for Object-Oriented Software Using Search-based Techniques". International Journal of Software Engineering and Knowledge Engineering 31, nr 02 (luty 2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.

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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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Kumaresh, Sakthi, i Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization". Journal of Communications Software and Systems 11, nr 1 (23.03.2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.

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

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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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Henderson, Craig. "Managing software defects". ACM SIGSOFT Software Engineering Notes 33, nr 4 (lipiec 2008): 1–3. http://dx.doi.org/10.1145/1384139.1384141.

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Han, Wan Jiang, He Yang Jiang, Yi Sun i Tian Bo Lu. "Software Defect Distribution Prediction for BOSS System". Applied Mechanics and Materials 701-702 (grudzień 2014): 67–70. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.67.

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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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Huh, Sang Moo, i Woo-Je Kim. "The Derivation of Defect Priorities and Core Defects through Impact Relationship Analysis between Embedded Software Defects". Applied Sciences 10, nr 19 (4.10.2020): 6946. http://dx.doi.org/10.3390/app10196946.

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As embedded software is closely related to hardware equipment, any defect in embedded software can lead to major accidents. Thus, all defects must be collected, classified, and tested based on their severity. In the pure software field, a method of deriving core defects already exists, enabling the collection and classification of all possible defects. However, in the embedded software field, studies that have collected and categorized relevant defects into an integrated perspective are scarce, and none of them have identified core defects. Therefore, the present study collected embedded software defects worldwide and identified 12 types of embedded software defect classifications through iterative consensus processes with embedded software experts. The impact relation map of the defects was drawn using the decision-making trial and evaluation laboratory (DEMATEL) method, which analyzes the influence relationship between elements. As a result of analyzing the impact relation map, the following core embedded software defects were derived: hardware interrupt, external interface, timing error, device error, and task management. All defects can be tested using this defect classification. Moreover, knowing the correct test order of all defects can eliminate critical defects and improve the reliability of embedded systems.
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Kumaresh, Sakthi, i R. Baskaran. "Software Defect Prevention through Orthogonal Defect Classification (ODC)". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, nr 3 (15.10.2013): 2393–400. http://dx.doi.org/10.24297/ijct.v11i3.1166.

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“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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Park, Jihyun, i Byoungju Choi. "Automatic Method for Distinguishing Hardware and Software Faults Based on Software Execution Data and Hardware Performance Counters". Electronics 9, nr 11 (2.11.2020): 1815. http://dx.doi.org/10.3390/electronics9111815.

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Debugging in an embedded system where hardware and software are tightly coupled and have restricted resources is far from trivial. When hardware defects appear as if they were software defects, determining the real source becomes challenging. In this study, we propose an automated method of distinguishing whether a defect originates from the hardware or software at the stage of integration testing of hardware and software. Our method overcomes the limitations of the embedded environment, minimizes the effects on runtime, and identifies defects by obtaining and analyzing software execution data and hardware performance counters. We analyze the effects of the proposed method through an empirical study. The experimental results reveal that our method can effectively distinguish defects.
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Falessi, Davide, Aalok Ahluwalia i Massimiliano DI Penta. "The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects". ACM Transactions on Software Engineering and Methodology 31, nr 1 (31.01.2022): 1–26. http://dx.doi.org/10.1145/3467895.

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Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore “snoring.” In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction.
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Pagadala, Srivyshnavi, Sony Bathala i B. Uma. "An Efficient Predictive Paradigm for Software Reliability". Asian Journal of Computer Science and Technology 8, S3 (5.06.2019): 114–16. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2051.

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Software Estimation gives solution for complex problems in the software industry which gives estimates for cost and schedule. Software Estimation provides a comprehensive set of tips and heuristics that Software Developers, Technical Leads, and Project Managers can apply to create more accurate estimates. It presents key estimation strategies and addresses particular estimation challenges. In the planning of a software development project, a major challenge faced by project managers is to predict the defects and effort. The Software defect plays critical role in software product development. The estimation of defects can be determined in the product development using many advanced statistical modelling techniques based on the empirical data obtained by the testing phases. The proposed estimation technique in this paper is a model which was developed using Rayleigh function for estimating effect of defects in Software Project Management. The present study offers to decide how many defects creep in to production and determine the effort spent in months. The estimation model was used on Software Testing Life Cycle (STLC) to complete product. The accuracy of the model explains the variation in spent efforts in months associated with number of defects. The model helps the senior management in estimating the defects, schedule, cost and effort.
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Rozprawy doktorskie na temat "Software defects"

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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.

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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.
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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.

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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.

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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, i 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.

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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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Shippey, Thomas Joshua. "Exploiting abstract syntax trees to locate software defects". Thesis, University of Hertfordshire, 2015. http://hdl.handle.net/2299/16365.

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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.
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Zheng, Xue Lin. "A Framework for Early Detection of Requirements Defects". Thesis, Griffith University, 2008. http://hdl.handle.net/10072/366377.

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This thesis is about early detection of requirements defects. Software-centred systems’ defects can cause loss of life, loss of property, loss of data and economic losses. Requirements defects are a major source of system defects. The early detection of requirements defects prevents software-centred systems’ defects, and thus reduces the various types of losses. In the past thirty years, many methods have been developed to detect requirements defects. The most prominent methods include inspections, automated static analysis, simulation, formal specifications and more recently model-checking. Each method has different strengths and weaknesses. The lack of integration of the different detection techniques produces a knowledge gap that causes problems with repeatability, scalability, effectiveness, and efficiency of the detection process. This knowledge gap is enlarged by the lack of a well-specified defect classification scheme that specifies quality rules, collects defects, specifies defect patterns, and classifies the patterns. This thesis proposes a framework for early defect detection based on Behavior trees, a representation which makes it practical to integrate the various detection techniques. Individual requirements are translated one at a time into Requirements Behavior Trees. These Requirements Behavior Trees are then integrated into an Integrated Behavior Tree that can be inspected, statically analysed, model checked and simulated. The framework is based on the hypothesis that if a well-specified defect classification scheme is developed and different types of detectors are integrated to detect patterns that suit their capabilities and if processes are developed to cover the complete requirements lifecycle, then the framework’s detection results will be more effective and more efficient, and the results will be more repeatable and scalable than existing methods. The framework includes a Behavior Trees defect classification scheme. The scheme defines defect patterns for requirements written in English and requirements specified by Behavior Trees. The scheme has a variety of defect patterns. Each defect pattern contains the characteristics of a type of defect. Defect patterns are grouped together based on the quality rules that they violate. This framework and the hypothesis have been tested using four case studies. The results of the case studies found that compared to the Perspective-based Reading method and three conventional requirements analysis methods the framework proposed was more effective and able to detect a broader range of defect types. However, because of the lack of the tool support, the efficiency of the method is still questionable. It should however improve with better tool support.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
Full Text
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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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Almossawi, Ali. "Investigating the architectural drivers of defects in open-source software systems : an empirical study of defects and reopened defects in GNOME". Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76566.

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Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 64-67).
In major software systems that are developed by competent software engineers, the existence of defects in production is unlikely to be an acceptable situation. And yet, we find that in several such systems, defects remain a reality. Furthermore, the number of changes that are fixed only to then be reopened is noticeable. The implications of having defects in a system can be frustrating for all stakeholders, and when they require constant rework, they can lead to the problematic code-test-code-test mode of development. For management, such conditions can result in slipped schedules and an increase in development costs and for upper management and users, they can result in losing confidence in the product. This study looks at the drivers of defects in the mature open-source project GNOME and explores the relationship between the various drivers of these defects and software quality. Using defect-activity and source-code data for 32 systems over a period of eight years, the work presents a multiple regression model capable of explaining 16.2% of defects and a logistic regression model capable of explaining between 13.6% and 18.1% of reopened defects. The study also shows that although defects in general and reopened defects appear to move together, defects in general correlate with a measure of complexity that captures how components connect to each other whereas reopened defects correlate with a measure that captures the inner complexities of components, thereby suggesting that different types of defects are correlated with different forms of complexity.
by Ali Almossawi.
S.M.in Engineering and Management
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Vandehei, Bailey R. "Leveraging Defects Life-Cycle for Labeling Defective Classes". DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2111.

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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.
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Arantes, Alessandro Oliveira. "REACTOR: Combining static analysis, testing and reverse engineering to detect software defects". Instituto Nacional de Pesquisas Espaciais (INPE), 2016. http://urlib.net/sid.inpe.br/mtc-m21b/2016/04.20.19.30.

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It is increasingly common the use of computer systems to replace human labor in critical systems, and since these systems have become more autonomous in decision making, they demand a high degree of quality and robustness. INPE develops embedded systems for scientific satellites and stratospheric balloons; consequently, the process of verification and validation require special care in detecting and preventing defects. In terms of complexity and system${'}$s domain in question, these processes consume specialists manpower for a long period. In this scenario, the application of techniques that can automatically support test process provide a significant gain in specialists productivity and efficiency. For this purpose, this work performs the source code reverse engineering in order to support a combination of two V\&V processes, static source code analysis and software testing, in order to detect a wider range of defects. The proposed method, called REACTOR (Reverse Engineering for stAtic Code analysis and Testing to detect sOftwaRe defects), complements the traditional way that static code analyzers work by using dynamic information obtained by an automated test case generator, which combines three different black box techniques, being also possible to infer a set of estimated expected results similar to a test oracle. However, the combination of such techniques is not trivial, especially in terms of tasks that commonly demand some action that are not easily automated. Furthermore, the static analysis by itself can not reveal several types of defects that can only be detected by combining static analysis and dynamic information. The REACTOR method has been implemented in a software tool, also called REACTOR, which exempts from a large manual labors amount from testers by automating the process and basing only on applications source code. In addition, REACTOR was applied to some case studies including one of the space application domain, and it performed better than three other well known static code analyzers.
É cada vez mais comum a utilização de sistemas computacionais em substituição à mão de obra humana em sistemas críticos, e na medida em que estes sistemas têm se tornado mais autônomos para tomar decisões, eles exigem um alto grau de qualidade e robustez. O INPE desenvolve sistemas embarcados para satélites científicos e balões estratosféricos; consequentemente, os processos de verificação e validação exigem cuidados especiais na detecção e prevenção de defeitos. E tendo em vista a complexidade e o domínio dos sistemas em questão, estes processos consomem a mão de obra especialista por um longo período. Neste cenário, a aplicação de técnicas que possam efetuar testes de forma automática auxiliam o processo proporcionando um ganho significativo de produtividade e eficácia no trabalho dos especialistas. Com esse objetivo, este trabalho realiza a engenharia reversa de código-fonte de modo a combinar dois processos de V\&V, análise estática de código fonte e teste de software, a fim de detectar uma gama mais ampla de defeitos. O método proposto, denominado REACTOR (Reverse Engineering for stAtic Code analysis and Testing to detect sOftwaRe defects), complementa a maneira tradicional pela qual os analisadores de código estático trabalham usando informações dinâmicas obtidas por um gerador de caso de teste automatizado, que combina três técnicas de caixa preta diferentes, sendo também possível inferir um conjunto de resultados esperados estimados similar a um oráculo de teste. Ainda assim, a leitura do código fonte estático por si só pode não revelar vários tipos de defeitos que só podem ser detectados combinando a análise estática com informação dinâmica. O método REACTOR foi implementado em uma ferramenta de software, também chamado de REACTOR, que poupa os testadores de um grande volume de trabalho manual automatizando o processo e baseando-se apenas no código fonte. Além disso, a REACTOR foi aplicada em alguns casos de estudo incluindo uma aplicação da área espacial, e seu desempenho foi melhor do que outras três conhecidos analisadores de código estático.
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Książki na temat "Software defects"

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Glitch: The hidden impact of faulty software. Upper Saddle River, NJ: Prentice Hall, 2010.

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The software conspiracy: Why software companies put out faulty products, how they can hurt you, and what you can do about it. New York: McGraw-Hill, 2000.

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Perry, William E. A standard for auditing computer applications: Auditing information services defects. Boston: Auerbach, 1996.

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

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

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

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

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

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

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Części książek na temat "Software defects"

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Kumar, Sushil, Meera Sharma, S. K. Muttoo i V. B. Singh. "Autoclassify Software Defects Using Orthogonal Defect Classification". W Computational Science and Its Applications – ICCSA 2022 Workshops, 313–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10548-7_23.

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Beningo, Jacob. "Jump-Starting Software Development to Minimize Defects". W Embedded Software Design, 241–56. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8279-3_10.

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Hui, Zhanwei, Song Huang, Zhengping Ren i Yi Yao. "Review of Software Security Defects Taxonomy". W Lecture Notes in Computer Science, 310–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16248-0_46.

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Sharma, Kanta Prasad, Vinesh Kumar i Dac-Nhuong Le. "Defects Maintainability Prediction of the Software". W Optimization of Automated Software Testing Using Meta-Heuristic Techniques, 155–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07297-0_10.

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Holling, Dominik, Daniel Méndez Fernández i Alexander Pretschner. "A Field Study on the Elicitation and Classification of Defects for Defect Models". W Product-Focused Software Process Improvement, 380–96. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26844-6_28.

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Kessentini, Marouane, Houari Sahraoui, Mounir Boukadoum i Manuel Wimmer. "Search-Based Design Defects Detection by Example". W Fundamental Approaches to Software Engineering, 401–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19811-3_28.

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Mahouachi, Rim, Marouane Kessentini i Khaled Ghedira. "A New Design Defects Classification: Marrying Detection and Correction". W Fundamental Approaches to Software Engineering, 455–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28872-2_31.

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He, Lei, Juan Li, Qing Wang i Ye Yang. "Predicting Upgrade Project Defects Based on Enhancement Requirements: An Empirical Study". W Trustworthy Software Development Processes, 268–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01680-6_25.

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Rana, Zeeshan A., Sehrish Abdul Malik, Shafay Shamail i Mian M. Awais. "Identifying Association between Longer Itemsets and Software Defects". W Neural Information Processing, 133–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42051-1_18.

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Yang, Peng. "Software Defects Detecting Method Based on Data Mining". W Advances in Computer Science, Environment, Ecoinformatics, and Education, 272–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23324-1_44.

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Streszczenia konferencji na temat "Software defects"

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

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Sasso, Tommaso Dal. "Managing Software Defects". W 2014 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2014. http://dx.doi.org/10.1109/icsme.2014.124.

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Janusz, Sosnowski, i Maciej Korpalski. "Correlating software metrics with software defects". W Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, redaktorzy Ryszard S. Romaniuk i Maciej Linczuk. SPIE, 2018. http://dx.doi.org/10.1117/12.2501150.

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Vescan, Andreea, Camelia Serban i Gloria Cerasela Crisan. "Software Defects Rules Discovery". W 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2021. http://dx.doi.org/10.1109/icstw52544.2021.00028.

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Cassels, J. J. "Modeling IC Defects Using Circuit Simulation Software". W ISTFA 1996. ASM International, 1996. http://dx.doi.org/10.31399/asm.cp.istfa1996p0133.

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Abstract Often in the course of performing root cause failure analysis and fault localization, it can be helpful to have supporting information in the way of a defect model. This is particularly true when physical identification of a defect is unsuccessful. By modeling suspected, theorized, or documented defects in microchip circuitry, the analyst can more clearly show a direct link between defect and circuit failure in support of analysis conclusions.
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Gandini, Sergio, Danilo Ravotto, Walter Ruzzarin, Ernesto Sanchez, Giovanni Squillero i Alberto Tonda. "Automatic detection of software defects". W the 11th Annual conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570238.

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Wu, Binghui Helen. "Modeling defects in software systems". W 2011 IEEE International Conference on Granular Computing (GrC-2011). IEEE, 2011. http://dx.doi.org/10.1109/grc.2011.6122690.

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Ciborowska, Agnieszka, Aleksandar Chakarov i Rahul Pandita. "Contemporary COBOL: Developers' Perspectives on Defects and Defect Location". W 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2021. http://dx.doi.org/10.1109/icsme52107.2021.00027.

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Yang, Zhao Hong, Yun Zhan Gong, Qing Xiao i Ya Wen Wang. "DTS - A Software Defects Testing System". W 2008 Eighth IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2008). IEEE, 2008. http://dx.doi.org/10.1109/scam.2008.12.

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Kour, George, Shaul Strachan i Raz Regev. "Estimating Handling Time of Software Defects". W Fourth International Conference on Computer Science and Information Technology. Academy & Industry Research Collaboration Center (AIRCC), 2017. http://dx.doi.org/10.5121/csit.2017.70413.

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Raporty organizacyjne na temat "Software defects"

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

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

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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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