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.

Informação relacionada a concertos de bugs tem sido explorada na construção de preditores de bugs cuja função é o suporte para a verificação de sistemas de software identificando quais elementos, como arquivos, são mais propensos a bugs. Uma grande variedade de métricas estáticas de código e métricas de mudança já foi utilizada para construir tais preditores. Dos muitos preditores de bugs propostos, a grande maioria foca em sistemas orientados à objeto. Apesar de orientação a objetos ser o paradigma de escolha para a maioria das aplicações, o paradigma procedural ainda é usado em várias — muitas vezes cruciais — aplicações, como sistemas operacionais e sistemas embarcados. Portanto, eles também merecem atenção. Essa dissertação extende o trabalho na área de predição de bugs ao avaliar e aprimorar preditores de bugs para sistemas procedurais de software. Nós proporcionamos três principais contribuições: (i) comparação das abordagens existentes de predição de bugs no contexto de sistemas procedurais, (ii) proposta de uso dos atributos de qualidade de software como atributos de predição no contexto estudado e (iii) avaliação dos atributos propostos em conjunto com a melhor abordagem encontrada em (i). Nosso trabalho provê, portanto, fundamentos para melhorar a performance de preditores de bugs no contexto de sistemas procedurais. / Information regarding bug fixes has been explored to build bug predictors, which provide support for the verification of software systems, by identifying fault-prone elements, such as files. A wide range of static and change metrics have been used as features to build such predictors. Many bug predictors have been proposed, and their main target is objectoriented systems. Although object-orientation is currently the choice for most of the software applications, the procedural paradigm is still being used in many—sometimes crucial—applications, such as operating systems and embedded systems. Consequently, they also deserve attention. This dissertation extends work on bug prediction by evaluating and tailoring bug predictors to procedural software systems. We provide three key contributions: (i) comparison of bug prediction approaches in context of procedural software systems, (ii) proposal of the use of software quality features as prediction features in the studied context, and (iii) evaluation of the proposed features in association with the best approach found in (i). Our work thus provides foundations for improving the bug prediction performance in the context of procedural software systems.

Informação relacionada a concertos de bugs tem sido explorada na construção de preditores de bugs cuja função é o suporte para a verificação de sistemas de software identificando quais elementos, como arquivos, são mais propensos a bugs. Uma grande variedade de métricas estáticas de código e métricas de mudança já foi utilizada para construir tais preditores. Dos muitos preditores de bugs propostos, a grande maioria foca em sistemas orientados à objeto. Apesar de orientação a objetos ser o paradigma de escolha para a maioria das aplicações, o paradigma procedural ainda é usado em várias — muitas vezes cruciais — aplicações, como sistemas operacionais e sistemas embarcados. Portanto, eles também merecem atenção. Essa dissertação extende o trabalho na área de predição de bugs ao avaliar e aprimorar preditores de bugs para sistemas procedurais de software. Nós proporcionamos três principais contribuições: (i) comparação das abordagens existentes de predição de bugs no contexto de sistemas procedurais, (ii) proposta de uso dos atributos de qualidade de software como atributos de predição no contexto estudado e (iii) avaliação dos atributos propostos em conjunto com a melhor abordagem encontrada em (i). Nosso trabalho provê, portanto, fundamentos para melhorar a performance de preditores de bugs no contexto de sistemas procedurais. / Information regarding bug fixes has been explored to build bug predictors, which provide support for the verification of software systems, by identifying fault-prone elements, such as files. A wide range of static and change metrics have been used as features to build such predictors. Many bug predictors have been proposed, and their main target is objectoriented systems. Although object-orientation is currently the choice for most of the software applications, the procedural paradigm is still being used in many—sometimes crucial—applications, such as operating systems and embedded systems. Consequently, they also deserve attention. This dissertation extends work on bug prediction by evaluating and tailoring bug predictors to procedural software systems. We provide three key contributions: (i) comparison of bug prediction approaches in context of procedural software systems, (ii) proposal of the use of software quality features as prediction features in the studied context, and (iii) evaluation of the proposed features in association with the best approach found in (i). Our work thus provides foundations for improving the bug prediction performance in the context of procedural software systems.

Informação relacionada a concertos de bugs tem sido explorada na construção de preditores de bugs cuja função é o suporte para a verificação de sistemas de software identificando quais elementos, como arquivos, são mais propensos a bugs. Uma grande variedade de métricas estáticas de código e métricas de mudança já foi utilizada para construir tais preditores. Dos muitos preditores de bugs propostos, a grande maioria foca em sistemas orientados à objeto. Apesar de orientação a objetos ser o paradigma de escolha para a maioria das aplicações, o paradigma procedural ainda é usado em várias — muitas vezes cruciais — aplicações, como sistemas operacionais e sistemas embarcados. Portanto, eles também merecem atenção. Essa dissertação extende o trabalho na área de predição de bugs ao avaliar e aprimorar preditores de bugs para sistemas procedurais de software. Nós proporcionamos três principais contribuições: (i) comparação das abordagens existentes de predição de bugs no contexto de sistemas procedurais, (ii) proposta de uso dos atributos de qualidade de software como atributos de predição no contexto estudado e (iii) avaliação dos atributos propostos em conjunto com a melhor abordagem encontrada em (i). Nosso trabalho provê, portanto, fundamentos para melhorar a performance de preditores de bugs no contexto de sistemas procedurais. / Information regarding bug fixes has been explored to build bug predictors, which provide support for the verification of software systems, by identifying fault-prone elements, such as files. A wide range of static and change metrics have been used as features to build such predictors. Many bug predictors have been proposed, and their main target is objectoriented systems. Although object-orientation is currently the choice for most of the software applications, the procedural paradigm is still being used in many—sometimes crucial—applications, such as operating systems and embedded systems. Consequently, they also deserve attention. This dissertation extends work on bug prediction by evaluating and tailoring bug predictors to procedural software systems. We provide three key contributions: (i) comparison of bug prediction approaches in context of procedural software systems, (ii) proposal of the use of software quality features as prediction features in the studied context, and (iii) evaluation of the proposed features in association with the best approach found in (i). Our work thus provides foundations for improving the bug prediction performance in the context of procedural software systems.

Software fault prediction (SFP) has an important role in the process of improving software product quality by identifying fault-prone modules. Constructing quality models includes a usage of metrics that describe real world entities defined by numbers or attributes. Examining the nature of machine learning (ML), researchers proposed its algorithms as suitable for fault prediction. Moreover, information that software metrics contain will be used as statistical data necessary to build models for a certain ML algorithm. One of the most used ML algorithms is a Bayesian network (BN), which is represented as a graph, with a set of variables and relations between them. This thesis will be focused on the usage of process and static code metrics with BN learners for SFP. First, we provided an informal review on non-static code metrics. Furthermore, we created models that contained different combinations of process and static code metrics, and then we used them to conduct an experiment. The results of the experiment were statistically analyzed using a non-parametric test, the Kruskal-Wallis test. The informal review reported that non-static code metrics are beneficial for the prediction process and its usage is highly recommended for industrial projects. Finally, experimental results did not provide a conclusion which process metric gives a statistically significant result; therefore, a further investigation is needed.

Indiana University-Purdue University Indianapolis (IUPUI) / This dissertation proposes an approach to reduce the cost of manual inspections for as large a number of false positive warnings that are being reported by Static Code Analysis (SCA) tools as much as possible using Machine Learning (ML) techniques. The proposed approach neither assume to use the particular SCA tools nor depends on the specific programming language used to write the target source code or the application. To reduce the number of false positive warnings we first evaluated a number of SCA tools in terms of software engineering metrics using a highlighted synthetic source code named the Juliet test suite. From this evaluation, we concluded that the SCA tools report plenty of false positive warnings that need a manual inspection. Then we generated a number of datasets from the source code that forced the SCA tool to generate either true positive, false positive, or false negative warnings. The datasets, then, were used to train four of ML classifiers in order to classify the collected warnings from the synthetic source code. From the experimental results of the ML classifiers, we observed that the classifier that built using the Random Forests (RF) technique outperformed the rest of the classifiers. Lastly, using this classifier and an instance-based transfer learning technique, we ranked a number of warnings that were aggregated from various open-source software projects. The experimental results show that the proposed approach to reduce the cost of the manual inspection of the false positive warnings outperformed the random ranking algorithm and was highly correlated with the ranked list that the optimal ranking algorithm generated.

Nesta tese de doutorado, apresentamos uma abordagem para a observação das métricas de código-fonte, estudando-as através de suas distribuições e associações, além de discutir as relações de causalidade e implicações práticas-gerenciais para monitoramento das mesmas. Em nossos estudos avaliamos a distribuição e correlações dos valores das métricas de 38 projetos de software livre, dentre os com mais contribuidores ativos em seus repositórios. Para tal, coletamos e analisamos os valores para cada métrica em mais de 344.872 classes e módulos dos projetos avaliados. Complementarmente, para mostrarmos a utilidade do monitoramento de métricas, descrevemos uma extensão e adaptação do modelo de causalidade do conceito de atratividade de projetos de software livre, que indica uma relação estatística entre os valores das métricas de código-fonte e a quantidade de downloads, contribuidores e atualizações (commits) nos repositórios dos projetos. Para isso, realizamos estudos empíricos com milhares de projetos de software livre. Do ponto de vista prático, também contribuímos com um conjunto de ferramentas inovador para a automação da avaliação de projetos de software livre, com ênfase nos estudos e na seleção de métricas, o que permite a análise de código-fonte de acordo com a percepção de qualidade das comunidades de software livre. Entre as principais contribuições desta tese está uma análise detalhada, em relação ao comportamento, valores e estudos de caso, de 15 métricas de código-fonte, o que representa um avanço em comparação a literatura relacionada ao ampliar o número de métricas avaliadas e propor uma abordagem que visa diminuir as contradições das análises das métricas. / In this Ph.D dissertation we present an approach about source code metrics tracking. We have researched source code metrics distributions and associations to discuss their causality and management-practices implications. Our studies have assessed distributions and correlations of source code metric values on 38 free software projects, which have a lot of activated contributors in their repositories. We have collected and analyzed metrics from 344,872 classes and modules of about 38 free software projects. Additionally, to show how it is useful to track source code metrics, we have extended the model of free software attractiveness to include source code metrics. Our technical attractiveness model indicates a statistical relationship between source code metrics and number of downloads, contributors, and commits in the analyzed free software repositories. For that, we have conducted empirical studies with 8,450 free software projects. From a practical point of view, we have contributed with a set of innovative tools for automated evaluation of free software projects. Our tool allow the analyses of source code metrics that mirror quality perceptions from the free software communities point of view.

Software development can be thought of in two fairly distinct ways: on one hand, it is a scientific area in which scientific method is applied in terms of quantifiable measurements and empirical studies. On the other hand (as with many other principles) it is based on craftsmanship in which the best practices emerge with experience.TDD is one such practice, emerging from the community of software developers as a means of developing higher quality software. This thesis aimed to study whether or not TDD actually leads to an increase in quality. This was conducted by developing a client application for a company in southern Sweden called TN Datakonsult AB. The application receives and visualizes signals from industrial processes. An API with the intent to capture this data over HTTP was developed in C#. This API was written by using TDD, while the client that consumed the API was written without tests as a control group. The code metrics that were calculated were cyclomatic complexity, lines of code, depth of inheritance, code coverage and class coupling. The results shows that many of the benefits associated with TDD are derived from the ability to track that the application under development is behaving as expected at any given time. This is a quality aspect which is particularly difficult to measure, even though the code metrics pre-sented will assist the developer to keep track of the state of the application.

Nesta tese de doutorado, apresentamos uma abordagem para a observação das métricas de código-fonte, estudando-as através de suas distribuições e associações, além de discutir as relações de causalidade e implicações práticas-gerenciais para monitoramento das mesmas. Em nossos estudos avaliamos a distribuição e correlações dos valores das métricas de 38 projetos de software livre, dentre os com mais contribuidores ativos em seus repositórios. Para tal, coletamos e analisamos os valores para cada métrica em mais de 344.872 classes e módulos dos projetos avaliados. Complementarmente, para mostrarmos a utilidade do monitoramento de métricas, descrevemos uma extensão e adaptação do modelo de causalidade do conceito de atratividade de projetos de software livre, que indica uma relação estatística entre os valores das métricas de código-fonte e a quantidade de downloads, contribuidores e atualizações (commits) nos repositórios dos projetos. Para isso, realizamos estudos empíricos com milhares de projetos de software livre. Do ponto de vista prático, também contribuímos com um conjunto de ferramentas inovador para a automação da avaliação de projetos de software livre, com ênfase nos estudos e na seleção de métricas, o que permite a análise de código-fonte de acordo com a percepção de qualidade das comunidades de software livre. Entre as principais contribuições desta tese está uma análise detalhada, em relação ao comportamento, valores e estudos de caso, de 15 métricas de código-fonte, o que representa um avanço em comparação a literatura relacionada ao ampliar o número de métricas avaliadas e propor uma abordagem que visa diminuir as contradições das análises das métricas. / In this Ph.D dissertation we present an approach about source code metrics tracking. We have researched source code metrics distributions and associations to discuss their causality and management-practices implications. Our studies have assessed distributions and correlations of source code metric values on 38 free software projects, which have a lot of activated contributors in their repositories. We have collected and analyzed metrics from 344,872 classes and modules of about 38 free software projects. Additionally, to show how it is useful to track source code metrics, we have extended the model of free software attractiveness to include source code metrics. Our technical attractiveness model indicates a statistical relationship between source code metrics and number of downloads, contributors, and commits in the analyzed free software repositories. For that, we have conducted empirical studies with 8,450 free software projects. From a practical point of view, we have contributed with a set of innovative tools for automated evaluation of free software projects. Our tool allow the analyses of source code metrics that mirror quality perceptions from the free software communities point of view.

<p>This dissertation proposes an approach to reduce the cost of manual inspections for as large a number of false positive warnings that are being reported by Static Code Analysis (SCA) tools as much as possible using Machine Learning (ML) techniques. The proposed approach neither assume to use the particular SCA tools nor depends on the specific programming language used to write the target source code or the application. To reduce the number of false positive warnings we first evaluated a number of SCA tools in terms of software engineering metrics using a highlighted synthetic source code named the Juliet test suite. From this evaluation, we concluded that the SCA tools report plenty of false positive warnings that need a manual inspection. Then we generated a number of datasets from the source code that forced the SCA tool to generate either true positive, false positive, or false negative warnings. The datasets, then, were used to train four of ML classifiers in order to classify the collected warnings from the synthetic source code. From the experimental results of the ML classifiers, we observed that the classifier that built using the Random Forests</p> <p>(RF) technique outperformed the rest of the classifiers. Lastly, using this classifier and an instance-based transfer learning technique, we ranked a number of warnings that were aggregated from various open-source software projects. The experimental results show that the proposed approach to reduce the cost of the manual inspection of the false positive warnings outperformed the random ranking algorithm and was highly correlated with the ranked list that the optimal ranking algorithm generated.</p>