Context. Human oracle cost, the cost associated in estimating the correctness of the output for the given test inputs is manually evaluated by humans and this cost is significant and is a concern in the software test data generation field. This study has been designed in the context to assess metrics that might predict human oracle cost. Objectives. The major objective of this study is to address the human oracle cost, for this the study identifies the metrics that are good predictors of human oracle cost and can further help to solve the oracle problem. In this process, the identified suitable metrics from the literature are applied on the test input, to see if they can help in predicting the correctness of the output for the given test input. Methods. Initially a literature review was conducted to find some of the metrics that are relevant to the test data. Besides finding the aforementioned metrics, our literature review also tries to find out some possible code metrics that can be ap- plied on test data. Before conducting the actual experiment two pilot experiments were conducted. To accomplish our research objectives an experiment is conducted in the BTH university with master students as sample population. Further group interviews were conducted to check if the participants perceive any new metrics that might impact the correctness of the output. The data obtained from the experiment and the interviews is analyzed using linear regression model in SPSS suite. Further to analyze the accuracy vs metric data, linear discriminant model using SPSS pro- gram suite was used. Results.Our literature review resulted in 4 metrics that are suitable to our study. As our test input is HTML we took HTML depth, size, compression size, number of tags as our metrics. Also, from the group interviews another 4 metrics are drawn namely number of lines of code and number of <div>, anchor <a> and paragraph <p> tags as each individual metric. The linear regression model which analyses time vs metric data, shows significant results, but with multicollinearity effecting the result, there was no variance among the considered metrics. So, the results of our study are proposed by adjusting the multicollinearity. Besides, the above analysis, linear discriminant model which analyses accuracy vs metric data was conducted to predict the metrics that influences accuracy. The results of our study show that metrics positively correlate with time and accuracy. Conclusions. From the time vs metric data, when multicollinearity is adjusted by applying step-wise regression reduction technique, the program size, compression size and <div> tag are influencing the time taken by sample population. From accuracy vs metrics data number of <div> tags and number of lines of code are influencing the accuracy of the sample population.

We live in a time where software is used everywhere. It is used even for creating other software by helping developers with writing or generating new code. To do this properly, metrics to measure software quality are being used to evaluate the final code. However, they are sometimes too costly to compute, or simply don't have the expected effect. Therefore, new and better ways of software evaluation are needed. In this research, we are investigating the usage of the statistical approaches used commonly in the natural language processing (NLP) area. In order to introduce and evaluate new metrics, a Java N-gram language model is created from a large Java language code corpus. Naturalness, a method-level metric, is introduced and calculated for chosen projects. The correlation with well-known software complexity metrics are calculated and discussed. The results, however, show that the metric, in the form that we have defined it, is not suitable for software complexity evaluation since it is highly correlated with a well-known metric (token count), which is much easier to compute. Different definition of the metric is suggested, which could be a target of future study and research.

Two tasks that software engineers constantly perform are writing code that is easy to evolve and maintain, and detecting poorly written pieces of code. For the former, software engineers commonly rely on well-known software architecture styles, such as Model-View-Controller (MVC). To the latter, they rely on code metrics and code smell detection approaches. However, up to now, these code metrics and code smell approaches do not take into account underlying architectureall classes are assessed as if they were the same. In practice, software developers know that classes differ in terms of responsibilities and implementation, and thus, we expect these classes to present different levels of coupling, cohesion, and complexity. As an example, in an MVC system, Controllers are responsible for the flow between the Model and the View, and Models are responsible for representing the systems business concepts. Thus, in this thesis, we evaluate the impact of architectural roles within a system architecture on code metrics and code smells. We performed an empirical analysis in 120 open source systems, and interviewed and surveyed more than 50 software developers. Our findings show that each architectural role has a different code metric values distribution, which is a likely consequence of their specific responsibilities. Thus, we propose SATT, an approach that provides specific thresholds for architectural roles that are significantly different from others in terms of code smells. We also show that classes that play a specific architectural role contain specific code smells, which developers perceive as problems, and can impact class\' change- and defect-proneness. Based on our findings, we suggest that developers understand the responsibilities of each architectural role in their system architecture, so that code metrics and code smells techniques can provide more accurate feedback. / Duas tarefas que desenvolvedores de software constantemente fazem são escrever código fácil de ser mantido e evoluído, e detectar pedaços de código problemáticos. Para a primeira tarefa, desenvolvedores comumente fazem uso de conhecidos padrões arquiteturais, como Model-View-Controller (MVC). Para a segunda tarefa, desenvolvedores fazem uso de métricas de código e estratégias de detecção de maus cheiros de código (code smells). No entanto, até o momento, métricas de código e estratégias de detecção de maus cheiros de código não levam em conta a arquitetura do software em análise. Isso significa que todas classes são avaliadas como se umas fossem iguais às outras. Na prática, sabemos que classes são diferentes em suas responsibilidades e implementação, e portanto, esperamos que elas variem em termos de acoplamento, coesão e complexidade. Por exemplo, em um sistema MVC, Controladores são responsáveis pelo fluxo entre a camada de Modelo e a camada de Visão, e Modelos representam a visão de negócios do sistema. Nesta tese, nós avaliamos o impacto dos papéis arquiteturais em técnicas de medição de métricas de código e de detecção de maus cheiros de código. Nós realizamos um estudo empírico em 120 sistemas de código aberto, e entrevistamos e realizamos questionários com mais de 50 desenvolvedores. Nossos resultados mostram que cada papel arquitetural possui distribuições diferentes de valores de métrica de código, consequência das diferentes responsabilidades de cada papel. Como consequência, propomos SATT, uma abordagem que provê thresholds específicos para papéis arquiteturais que são significantemente diferentes de outros em termos de métricas de código. Mostramos também que classes que cumprem um papel arquitetural específico também contêm maus cheiros de código específicos. Esses maus cheiros são percebidos por desenvolvedores como problemas reais e podem fazer com que essas classes sejam mais modificadas e apresentem mais defeitos do que classes limpas. Sugerimos então que desenvolvedores entendam a arquitetura dos seus sistemas, bem como as responsabilidades de cada papel arquitetural que as classes desempenham, para que tanto métricas de código quanto estratégias de detecção de maus cheiros de código possam prover um melhor retorno.

Two tasks that software engineers constantly perform are writing code that is easy to evolve and maintain, and detecting poorly written pieces of code. For the former, software engineers commonly rely on well-known software architecture styles, such as Model-View-Controller (MVC). To the latter, they rely on code metrics and code smell detection approaches. However, up to now, these code metrics and code smell approaches do not take into account underlying architectureall classes are assessed as if they were the same. In practice, software developers know that classes differ in terms of responsibilities and implementation, and thus, we expect these classes to present different levels of coupling, cohesion, and complexity. As an example, in an MVC system, Controllers are responsible for the flow between the Model and the View, and Models are responsible for representing the systems business concepts. Thus, in this thesis, we evaluate the impact of architectural roles within a system architecture on code metrics and code smells. We performed an empirical analysis in 120 open source systems, and interviewed and surveyed more than 50 software developers. Our findings show that each architectural role has a different code metric values distribution, which is a likely consequence of their specific responsibilities. Thus, we propose SATT, an approach that provides specific thresholds for architectural roles that are significantly different from others in terms of code smells. We also show that classes that play a specific architectural role contain specific code smells, which developers perceive as problems, and can impact class\' change- and defect-proneness. Based on our findings, we suggest that developers understand the responsibilities of each architectural role in their system architecture, so that code metrics and code smells techniques can provide more accurate feedback. / Duas tarefas que desenvolvedores de software constantemente fazem são escrever código fácil de ser mantido e evoluído, e detectar pedaços de código problemáticos. Para a primeira tarefa, desenvolvedores comumente fazem uso de conhecidos padrões arquiteturais, como Model-View-Controller (MVC). Para a segunda tarefa, desenvolvedores fazem uso de métricas de código e estratégias de detecção de maus cheiros de código (code smells). No entanto, até o momento, métricas de código e estratégias de detecção de maus cheiros de código não levam em conta a arquitetura do software em análise. Isso significa que todas classes são avaliadas como se umas fossem iguais às outras. Na prática, sabemos que classes são diferentes em suas responsibilidades e implementação, e portanto, esperamos que elas variem em termos de acoplamento, coesão e complexidade. Por exemplo, em um sistema MVC, Controladores são responsáveis pelo fluxo entre a camada de Modelo e a camada de Visão, e Modelos representam a visão de negócios do sistema. Nesta tese, nós avaliamos o impacto dos papéis arquiteturais em técnicas de medição de métricas de código e de detecção de maus cheiros de código. Nós realizamos um estudo empírico em 120 sistemas de código aberto, e entrevistamos e realizamos questionários com mais de 50 desenvolvedores. Nossos resultados mostram que cada papel arquitetural possui distribuições diferentes de valores de métrica de código, consequência das diferentes responsabilidades de cada papel. Como consequência, propomos SATT, uma abordagem que provê thresholds específicos para papéis arquiteturais que são significantemente diferentes de outros em termos de métricas de código. Mostramos também que classes que cumprem um papel arquitetural específico também contêm maus cheiros de código específicos. Esses maus cheiros são percebidos por desenvolvedores como problemas reais e podem fazer com que essas classes sejam mais modificadas e apresentem mais defeitos do que classes limpas. Sugerimos então que desenvolvedores entendam a arquitetura dos seus sistemas, bem como as responsabilidades de cada papel arquitetural que as classes desempenham, para que tanto métricas de código quanto estratégias de detecção de maus cheiros de código possam prover um melhor retorno.