O uso de abordagens de predição de mudanças conjuntas auxilia os desenvolvedores a encontrar artefatos que mudam conjuntamente em uma tarefa. No passado, pesquisadores utilizaram análise estrutural para construir modelos de predição. Mais recentemente, têm sido propostas abordagens que utilizam informações históricas e análise textual do código fonte. Apesar dos avanços obtidos, os desenvolvedores de software ainda não usam essas abordagens amplamente, presumidamente por conta do número de falsos positivos. A hipótese desta tese é que informações contextuais obtidas das tarefas, da comunicação dos desenvolvedores e das mudanças dos artefatos descrevem as circunstâncias e condições em que as mudanças conjuntas ocorrem e podem ser utilizadas para realizar a predição de mudanças conjuntas. O objetivo desta tese consiste em avaliar se o uso de informações contextuais melhora a predição de mudanças conjuntas entre dois arquivos em relação às regras de associação, que é uma estratégia frequentemente usada na literatura. Foram construídos modelos de predição específicos para cada par de arquivos, utilizando as informações contextuais em conjunto com o algoritmo de aprendizagem de máquina random forest. Os modelos de predição foram avaliados em 129 versões de 10 projetos de código aberto da Apache Software Foundation. Os resultados obtidos foram comparados com um modelo baseado em regras de associação. Além de avaliar o desempenho dos modelos de predição também foram investigadas a influência do modo de agrupamento dos dados para construção dos conjuntos de treinamento e teste e a relevância das informações contextuais. Os resultados indicam que os modelos baseados em informações contextuais predizem 88% das mudanças corretamente, contra 19% do modelo de regras de associação, indicando uma precisão 3 vezes maior. Os modelos criados com informações contextuais coletadas em cada versão do software apresentaram maior precisão que modelos construídos a partir de um conjunto arbitrário de tarefas. As informações contextuais mais relevantes foram: o número de linhas adicionadas ou modificadas, número de linhas removidas, code churn, que representa a soma das linhas adicionadas, modificadas e removidas durante um commit, número de palavras na descrição da tarefa, número de comentários e papel dos desenvolvedores na discussão, medido pelo valor do índice de intermediação (betweenness) da rede social de comunicação. Os desenvolvedores dos projetos foram consultados para avaliar a importância dos modelos de predição baseados em informações contextuais. Segundo esses desenvolvedores, os resultados obtidos ajudam desenvolvedores novatos no projeto, pois não têm conhecimento da arquitetura e normalmente não estão familiarizados com as mudanças dos artefatos durante a evolução do projeto. Modelos de predição baseados em informações contextuais a partir de mudanças de software são relativamente precisos e, consequentemente, podem ser usados para apoiar os desenvolvedores durante a realização de atividades de manutenção e evolução de software / Co-change prediction aims to make developers aware of which artifacts may change together with the artifact they are working on. In the past, researchers relied on structural analysis to build prediction models. More recently, hybrid approaches relying on historical information and textual analysis have been proposed. Despite the advances in the area, software developers still do not use these approaches widely, presumably because of the number of false recommendations. The hypothesis of this thesis is that contextual information of software changes collected from issues, developers\' communication, and commit metadata describe the circumstances and conditions under which a co-change occurs and this is useful to predict co-changes. The aim of this thesis is to use contextual information to build co-change prediction models improving the overall accuracy, especially decreasing the amount of false recommendations. We built predictive models specific for each pair of files using contextual information and the Random Forest machine learning algorithm. The approach was evaluated in 129 versions of 10 open source projects from the Apache Software Foundation. We compared our approach to a baseline model based on association rules, which is often used in the literature. We evaluated the performance of the prediction models, investigating the influence of data aggregation to build training and test sets, as well as the identification of the most relevant contextual information. The results indicate that models based on contextual information can correctly predict 88% of co-change instances, against 19% achieved by the association rules model. This indicates that models based on contextual information can be 3 times more accurate. Models created with contextual information collected in each software version were more accurate than models built from an arbitrary amount of contextual information collected from more than one version. The most important pieces of contextual information to build the prediction models were: number of lines of code added or modified, number of lines of code removed, code churn, number of words in the discussion and description of a task, number of comments, and role of developers in the discussion (measured by the closeness value obtained from the communication social network). We asked project developers about the relevance of the results obtained by the prediction models based on contextual information. According to them, the results can help new developers to the project, since these developers have no knowledge about the architecture and are usually not familiar with the artifacts history. Thus, our results indicate that prediction models based on the contextual information are useful to support developers during the maintenance and evolution activities

Degradação do design é um problema central investigado na área de evolução de software. A densa rede de interdependências que emerge entre classes e módulos ao longo do tempo resulta em código difícil de mudar, não reutilizável e que não comunica por si só sua intenção. Dentre outros motivos, designs degradam porque requisitos mudam de maneiras não antecipadas pelo design inicial, ou seja, as modificações no código introduzem dependências novas e não planejadas entre classes e módulos do sistema. A gerência de dependências visa reduzir a degradação do design por meio de uma série de mecanismos que auxiliam na administração da complexidade estrutural inerente de sistemas orientados a objetos. Neste trabalho, investigamos as técnicas de identificação de dependências estruturais e lógicas. Em particular, por meio de um estudo de larga escala, comparamos os conjuntos desses dois tipos de dependências. Em seguida, conduzimos um estudo de caso a fim de identificar as origens de dependências lógicas. Por fim, fazemos um levantamento das técnicas de visualização de dependências e mostramos a ferramenta XFlow. / Design degradation is a central problem investigated in the area of software evolution. The dense web of interdependencies that emerges among classes and modules over time results in code that is hard to change, not reusable and that does not communicate its intention. Among other reasons, designs degrade because requirements changes in ways that were not anticipated by the initial design, i.e. the changes in code introduce new and unplanned dependencies among classes and modules of the system. Dependency management aims to reduce design degradation by means of a series of mechanisms that helps in the management of the inherent structural complexity of object oriented systems. In this work, we investigate structural and logical dependencies identification techniques. In particular, by means of a large scale study, we compare the sets of these two kinds of dependencies. Afterwards, we conduct a case study in order to uncover the origins of logical dependencies. Finally, we survey dependency visualization techniques and present the XFlow tool.

Minestis software allows geological domain modeling and resource estimation through an efficient and simplified geostatistics-based workflow. It has been designed for all those, geologists, mining engineers or auditors, for whom quick production of quality models is at the heart of their concerns.

This research investigated the potential benefits, risks and challenges, innovation properties and viability of cloud computing for records management on an Australian organisation within the mining software development sector. This research involved the use of a case study results analysis as well as a literature analysis. The literature analysis identified the ten potential benefits of cloud computing, as well as the ten risks and challenges associated with cloud computing. It further identified aspects, which needed to be addressed when adopting cloud computing in order to promote innovation within an organisation. The case study analysis was compared against a literature review of ten potential benefits of cloud computing, as well as the ten risks and challenges associated with cloud computing. This was done in order to determine cloud computing’s viability for records management for Company X (The company in the case study). Cloud computing was found to be viable for Company X. However, there were certain aspects, which need to be discussed and clarified with the cloud service provider beforehand in order to mitigate possible risks and compliance issues. It is also recommended that a cloud service provider who complies with international standards, such as ISO 15489, be selected. The viability of cloud computing for organisations similar to Company X (mining software development) followed a related path. These organisations need to ensure that the service provider is compliant with laws in their local jurisdiction, such as Electronic Transactions Act 1999 (Australia, 2011:14-15), as well as laws where their data (in the cloud) may be hosted. The benefits, risks and challenges of records management and cloud computing are applicable to these similar organisations. However, mitigation of these risks needs to be discussed with a cloud service provider beforehand. From an innovation perspective, cloud computing is able to promote innovation within an organisation, if certain antecedents are dealt with. Furthermore, if cloud computing is successfully adopted then it should promote innovation within organisations. / Information Science / M. Inf.

Minestis software allows geological domain modeling and resource estimation through an efficient and simplified geostatistics-based workflow. It has been designed for all those, geologists, mining engineers or auditors, for whom quick production of quality models is at the heart of their concerns.

This research investigated the potential benefits, risks and challenges, innovation properties and viability of cloud computing for records management on an Australian organisation within the mining software development sector. This research involved the use of a case study results analysis as well as a literature analysis. The literature analysis identified the ten potential benefits of cloud computing, as well as the ten risks and challenges associated with cloud computing. It further identified aspects, which needed to be addressed when adopting cloud computing in order to promote innovation within an organisation. The case study analysis was compared against a literature review of ten potential benefits of cloud computing, as well as the ten risks and challenges associated with cloud computing. This was done in order to determine cloud computing’s viability for records management for Company X (The company in the case study). Cloud computing was found to be viable for Company X. However, there were certain aspects, which need to be discussed and clarified with the cloud service provider beforehand in order to mitigate possible risks and compliance issues. It is also recommended that a cloud service provider who complies with international standards, such as ISO 15489, be selected. The viability of cloud computing for organisations similar to Company X (mining software development) followed a related path. These organisations need to ensure that the service provider is compliant with laws in their local jurisdiction, such as Electronic Transactions Act 1999 (Australia, 2011:14-15), as well as laws where their data (in the cloud) may be hosted. The benefits, risks and challenges of records management and cloud computing are applicable to these similar organisations. However, mitigation of these risks needs to be discussed with a cloud service provider beforehand. From an innovation perspective, cloud computing is able to promote innovation within an organisation, if certain antecedents are dealt with. Furthermore, if cloud computing is successfully adopted then it should promote innovation within organisations. / Information Science / M. Inf.