An important issue in software evolution is the time and effort needed to understand existing applications. Reverse engineering software to recover behavioural models is difficult and is complicated due to the lack of a standardized way of extracting and visualizing knowledge. In this thesis, we study a technique for automatically extracting static and dynamic data from software, filtering and analysing the data, and visualizing the behavioural model of a selected feature of a software application. We also investigate the usefulness of the generated diagrams as documentation for the software. We present a literature review of studies that have used static and dynamic data analysis for software comprehension. A set of criteria is created, and each approach, including this thesis’ technique, is compared using those criteria. We propose an approach to simplify lengthy traces by filtering out software components that are too low level to give a high-level picture of the selected feature. We use static information to identify and remove small and simple (or uncomplicated) software components from the trace. We define a utility method as any element of a program designed for the convenience of the designer and implementer and intended to be accessed from multiple places within a certain scope of the program. Utilityhood is defined as the extent to which a particular method can be considered a utility. Utilityhood is calculated using different combinations of selected dynamic and static variables. Methods with high utilityhood values are detected and removed iteratively. By eliminating utilities, we are left with a much smaller trace which is then visualized using the Use Case Map (UCM) notation. UCM is a scenario language used to specify and explain behaviour of complex systems. By doing so, we can identify the algorithm that generates a UCM closest to the mental model of the designers. Although when analysing our results we did not identify an algorithm that was best in all cases, there is a trend in that three of the best four algorithms (out of a total of eight algorithms investigated) used method complexity and method lines of code in their parameters. We also validated the algorithm results by doing a comparison with a list of methods given to us by the creators of the software and doing precision and recall calculations. Seven out of the eight participants agreed or strongly agreed that using UCM diagrams to visualize reduced traces is valid approach, with none who disagreed.

Software maintenance is considered to be among the most difficult, lengthy and costly parts of a software application's life-cycle. Regardless of the nature of the software application and the software engineering efforts to reduce component coupling to minimum, dependencies between software components in applications will always exist and initiate software maintenance operations as they tend to threaten the "health" of the software system during the evolution of particular components. The situation is more serious with modern technologies and development paradigms, such as Service Oriented Architecture Systems and Cloud Computing that introduce larger software systems that consist of a substantial number of components which demonstrate numerous types of dependencies with each other. This work proposes a reference architecture and a corresponding software framework that can be used to model the dependencies between components in software systems and can support the verification of a set of policies that are derived from system dependencies and are relative to the software maintenance operations being applied. Dependency modelling is performed using configuration information from the system, as well as information harvested from component interface descriptions. The proposed approach has been applied to a medium scale SOA system, namely the SCA Travel Sample from Apache Software Foundation, and has been evaluated for performance in a configuration specification related to a simulated SOA system consisting to up to a thousand web services offered in a few hundred components.

No description available.

Thesis (M. Sc.)--Carleton University, 2003. / Includes bibliographical references (p. 77-79). Also available in electronic format on the Internet.

Thesis (M.S.)--Baylor University, 2008. / Includes bibliographical references (p. 110-112)

Thesis (M.S.)--Rochester Institute of Technology, 1993. / Typescript. Includes bibliographical references (vol. 1, leaves 155-160).

Thesis (Ph.D.)--Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Nos dias atuais, com o aumento da demanda de serviços de suporte, como, por exemplo, no campo da alta disponibilidade e do desempenho para o cliente e da demanda de custos mais baixos para as empresas de manutenção e hospedagem de software, a resolução de incidentes de software em um tempo menor junto ao cliente e a prevenção de defeitos tornaram-se um tópicos fundamentais. Além disso, a atividade de manutenção de software é uma das fases que consome mais tempo, esforço e consequentemente custo no ciclo de desenvolvimento de software. Balancear eficiência e custo torna-se um desafio para qualquer empresa de suporte em manutenção de software. A abordagem utilizada nesta pesquisa estabelece um processo para classificar defeitos e delinear um conjunto de melhores soluções para os defeitos classificados a partir do histórico de defeitos e a base de conhecimento do cliente. Esta classificação também separa complexidade de problemas para serem gerenciados pelo time de suporte mais adequado. O método base utilizado é o ODC (Classificação Ortogonal de Defeitos) e extensões voltadas ao suporte de software são propostas e utilizadas. Por meio desta pesquisa, é possível verificar se a classificação e associação de soluções podem acarretar em uma redução no tempo de atendimento dos incidentes de suporte. Foi observado em quatro amostras de dois clientes diferentes (X e Y) que utilizando a classificação dos defeitos, direcionamento correto aos times de suporte e agrupamento de soluções, promoveu uma redução no tempo de atendimento em 70% dos incidentes de suporte no cliente Y e 92,5% dos incidentes de suporte para o cliente X. A redução de tempo foi obtida pela redução no número de transferências entre os times de suporte e a redução de incidentes. O processo apresentado é incremental, pois é baseado no aumento das informações históricas e na eficácia das soluções propostas. Este método de soluções pode favorecer a redução dos recursos necessários para suportar sistemas computacionais em provedores de serviço. / Nowadays with the increasing of demand in support services such as high availability and performance faced by customers and low cost operations to software maintenance and hosting enterprises, the resolution of software incidents in less time and defect prevention turned a key point. Moreover, the software maintenance is one most time consuming and effort demanding and by consequence cost in software development life cycle. Balancing effectiveness and costs turn a challenge to any enterprise that manages support in software maintenance. The approach used on this research defines a process to classify defects and boundary a set of best solutions associated to these classes from defects history and customer knowledge base. Also this classification separates different problem complexities that can be handled to different support teams. The base method used is the ODC (Orthogonal Defect Classification). With this research is possible to verify that the classification and solutions associations with problem class can undertake a time reduction in incidents resolution in software maintenance. It was verified using four service provider samples in two different customers (X and Y) that classification of defects, correct support team redirection and best solution grouping helps on reduction of incidents resolution time between 70% of incidents to customer Y and 92,5 % of incidents to customer X. The reduction was reached with reduction in number of transfers between supporting teams and incidents number reduction. The process is incremental because it is unfolded from history information and from effectiveness into solutions purposed. This process can leverage human resources nedded to support computational systems in service providers.

A manutenção é uma fase do ciclo de vida do software reconhecida por demandar uma grande quantidade de esforço em comparação às outras, como o desenvolvimento. As tarefas da manutenção envolvem a modificação do software, mais especificamente os artefatos que o compõe. O fato de modificar um determinado artefato pode afetar outras partes do software, cujo fenômeno é conhecido como impacto de mudança. Técnicas e ferramentas para apoiar a análise de impacto de mudança geralmente são baseadas em tipos de acoplamentos e têm sido propostas por diversos trabalhos na literatura, como as Regras de Associação e Aprendizado de Máquina usando Classificação. No entanto, essas técnicas não foram avaliadas do ponto de vista prático. Motivado pela falta de uma avaliação dessa perspectiva, esse trabalho realizou um experimento com uma ferramenta que implementa tais técnicas. Para tanto, foi desenvolvida uma ferramenta para automatizar a execução dessas técnicas para realizar predição de mudanças conjuntas de artefatos e apresentá-las ao desenvolvedor, além de coletar o feedback dos desenvolvedores na ferramenta. Com a ferramenta desenvolvida, foi realizada uma prova de conceito com o uso da ferramenta em tarefas de manutenção (defeitos) do projeto de software livre da Apache denominado CXF, por colaboradores novatos representados pelos alunos do curso de Ciência da Computação. Apesar de depender do desempenho das técnicas de predição de mudanças conjuntas, os resultados mostraram evidências que a ferramenta pode apoiar colaboradores novatos e, também, diminuir o esforço para realizar uma tarefa de manutenção de software, comparando quando nenhuma técnica é usada. / Maintenance is a stage of the software life cycle recognized by demanding a lot of effort in comparison to others, such as development. The maintenance tasks involve software modification, specifically the artifacts that compose it. Modifying a particular artifact can affect other parts of the software. This phenomenon is known as change impact. Techniques and tools to support the change impact analysis are usually based on types of couplings and have been proposed by several studies in the literature, such as the Association Rules and Machine Learning using Classification. However, these techniques have not been evaluated from the practical point of view. Therefore, a tool was developed to automate the execution of these techniques to predict joint changes of artifacts and present them to the developer, as well as collecting developers’ feedback on the tool. With the developed tool, a proof of concept was performed using the tool in maintenance tasks (bugs) of the Apache free software project called CXF, by novice collaborators represented by the students of the Computer Science course. Although it depends on the performance of joint change prediction techniques, the results showed evidence that the tool can support novice collaborators and also decrease the effort to perform a software maintenance task, comparing when no technique is used.

L’existence de plusieurs copies d’un même fragment de code (nommées des clones dans lalittérature) dans un logiciel peut compliquer sa maintenance et son évolution. La duplication decode peut poser des problèmes de consistance, notamment lors de la propagation de correction debogues. La détection de clones est par conséquent un enjeu important pour préserver et améliorerla qualité logicielle, propriété primordiale pour le succès d’un logiciel.L’objectif général de cette thèse est de contribuer à l’usage des détecteurs de clones dans destâches de maintenance logicielle. Nous avons centré nos contributions sur deux axes de recherche.Premièrement, la méthodologie pour comparer et évaluer les détecteurs de clones, i.e. les benchmarksde clones. Nous avons empiriquement évalué un benchmark de clones et avons montré queles résultats dérivés de ce dernier n’étaient pas fiables. Nous avons également identifié des recommandationspour fiabiliser la construction de benchmarks de clones. Deuxièmement, la spécialisationdes détecteurs de clones dans des tâches de maintenance logicielle.Nous avons développé uneapproche spécialisée dans un langage et une tâche (la réingénierie) qui permet aux développeursd’identifier et de supprimer la duplication de code de leurs logiciels. Nous avons mené des étudesde cas avec des experts du domaine pour évaluer notre approche. / The existence of several copies of a same code fragment—called code clones in the literature—in a software can complicate its maintenance and evolution. Code duplication can lead to consistencyproblems, especially during bug fixes propagation. Code clone detection is therefore a majorconcern to maintain and improve software quality, which is an essential property for a software’ssuccess.The general objective of this thesis is to contribute to the use of code clone detection in softwaremaintenance tasks. We chose to focus our contributions on two research topics. Firstly, themethodology to compare and assess code clone detectors, i.e. clone benchmarks. We perform anempirical assessment of a clone benchmark and we found that results derived from this latter arenot reliable. We also identified recommendations to construct more reliable clone benchmarks.Secondly, the adaptation of code clone detectors in software maintenance tasks. We developed aspecialized approach in one language and one task—refactoring—allowing developers to identifyand remove code duplication in their softwares. We conducted case studies with domain experts toevaluate our approach.