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.

Introduction   Bugs in software is a problem that grows over time if they are not dealt with in an early stage, therefore it is desirable to find bugs as early as possible. Bugs usually correlate with low software quality, which can be measured with different code metrics. The goal of this thesis is to find out if machine learning can be used to predict bugs, using code metric trends.  Method   To achieve the thesis goal a program was developed, which will be called Bloodhound, that analyses code metric trends to predict bugs using the machine learning algorithm k nearest neighbour. The code metrics required to do so is extracted using the program cdbs, which in turn uses the program SonarQube to create the source code metrics.  Results   Bloodhound were trained with a time-frame of 42 days between the dates June 1, 2016 to July 13, 2016 containing 202 commits and 312 changed files from the JabRef repository. The files were changed on average 1.5 times. Bloodhound never found more than 25% of the bugs and of its bug predictions, was right at most 42% of the time.  Conclusion   Bloodhound did not succeed in predicting bugs. But that was most likely because the time frame was too short to generate any significant trends.

Software being developed today can have years worth of history and hundreds if notthousands of files involved in a single project. When trying to determine what parts ofthe code need maintenance or updating it can be difficult to determine what will beproblematic in the future. Hours spent on code that will not cause problems in thefuture could be better used in other areas. Before changes are made to a codebase, themost error-prone parts of the code should be identified. In this thesis a method forcomparing what factors contribute to future bugs is developed, as well as testing severalfactors extracted from version control metadata using this method. In addition, avisualization was made using tree maps to show the most problematic files in a readablemanner, effectively using the produced data in an application to predict future bugs. Itwas determined that Age of newest change, Changes with age reducing importance andPrevious bugfixes with age reducing importance were all the most impactful factors forpredicting future bugs but that different repositories worked best with differentcombinations of the mentioned factors.