Software developers must produce software that can be changed without the risk of degrading the software architecture. One way to address software changes is to classify their causes and effects. A software change classification mechanism allows engineers to develop a common approach for handling changes. This information can be used to show the potential impact of the change. The goal of this research is to develop a change classification scheme that can be used to address causes of architectural degradation. This scheme can be used to model the effects of changes to software architecture. This research also presents a study of the initial architecture change classification scheme. The results of the study indicated that the classification scheme was easy to use and provided some benefit to developers. In addition, the results provided some evidence that changes of different types (in this classification scheme) required different amounts of effort to implement.

Software maintenance is one of the most crucial aspects of software development. Software engineering researchers must develop practical solutions to handle the challenges presented in maintaining mature software systems. Research that addresses practical means of mitigating the risks involved when changing software, reducing the complexity of mature software systems, and eliminating the introduction of preventable bugs is paramount to today’s software engineering discipline. Giving software developers the information that they need to make quality decisions about changes that will negatively affect their software systems is a key aspect to mitigating those risks. This dissertation presents work performed to assist developers to collect and process data that plays a role in change decision-making during the maintenance phase. To address these problems, developers need a way to better understand the effects of a change prior to making the change. This research addresses the problems associated with increasing architectural complexity caused by software change using a twoold approach. The first approach is to characterize software changes to assess their architectural impact prior to their implementation. The second approach is to identify a set of architecture metrics that correlate to system quality and maintainability and to use these metrics to determine the level of difficulty involved in making a change. The two approaches have been combined and the results presented provide developers with a beneficial analysis framework that offers insight into the change process.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-01T12:38:10Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação-JoelsonIsidroDaSilvaAraújo.pdf: 1742206 bytes, checksum: c2b6f413666e50be58b963e8624a1386 (MD5) / Made available in DSpace on 2016-07-01T12:38:10Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação-JoelsonIsidroDaSilvaAraújo.pdf: 1742206 bytes, checksum: c2b6f413666e50be58b963e8624a1386 (MD5) Previous issue date: 2015-09-04 / CAPEs / Contexto: a mudança faz parte da evolução e durante o ciclo de vida do software a maior parte dos custos está associada a esta tarefa. Poder fazer previsões sobre os potenciais efeitos causados através de uma mudança é uma forma de minimizar esses custos. Neste contexto, surge então a Análise de Impacto (AI) para medir o esforço que será necessário à mudança e para nortear como realizar a mesma da maneira mais adequada, entretanto o resultado gerado pode ser insuficiente, pois é possível existirem erros na identificação dos elementos possivelmente impactados, não contemplando todos os problemas existentes. Objetivo: este trabalho tem como objetivo investigar o que se tem feito para permitir um resultado mais preciso na AI, gerando um catálogo de benefícios e limitações e propondo um guia de boas práticas, respondendo as perguntas de pesquisa – O que se sabe atualmente sobre os benefícios e limitações da AI em mudança de software? O que se tem feito para minimizar os erros gerados na análise? Método: para a condução da pesquisa fez-se necessário a busca de dados na literatura, através de uma pesquisa exploratória, por meio de uma revisão sistemática com o intuito de investigar as técnicas de AI relatadas em pesquisas dos últimos anos. Resultados: de posse dos dados resultantes da extração e análise dos dados, os resultados são: (1) evidências de técnicas existentes que conseguiram minimizar imprecisões nos resultados da análise, (2) geração de catálogo de benefícios e limitações em seu uso e ainda, um guia de propostas de boas práticas a serem adotadas para permitir que a análise apresente melhores resultados. Conclusão: os resultados fornecem uma melhor visão dos fatores que precisam ser melhorados e, além disso, possibilitaram a criação de um guia de boas práticas. Com isto, pretendemos contribuir fornecendo uma melhor compreensão sobre as técnicas existentes, de que forma melhorias vêm sendo propostas e quais práticas permitem a maximização dos resultados gerados através da análise de impacto. / Context: Changing is part of the evolution and during the software lifecycle most cost is associated with this task. Being able to make predictions about the potential effects caused by a change is a way to minimize these costs. In this context, the Impact Analisys (IA) can be used to measure the effort it will take to change and to guide how to do the same in the most appropriate way, however the results generated may be insufficient, it is possible to detect errors on the elements identification possibly impacted, not including all the existing problems. Objective: This study has the objetive to investigate what has been done to allow more accurate result in IA , generating a catalog of benefits and limitations and proposing a guide of good practice by answering the research questions - What is currently known about the benefits and IA limitations on software changes? What has been done to minimize errors generated in the analysis? Methodology: To conduct this research it is necessary to search data in the literature, through an exploratory research using a systematic review that will allow an investigation about the most IA techniques used in the last years. Results: With the data generated through the extration and analisys of data, the results are: evidences of techniques which can be used to minimize inaccuracies in test results, (2) generation of catalog of benefits and limitations in its use and also a good practice guide to be adopted to allow the analysis present better results. Conclusion: the expected results will provide a better view of the factors that need to be improved and, besides, will enable the creation of a good practice guide. With this, we intend to contribute by providing a better understanding of existing techniques, how improvements have been proposed and what practices has been used to improve the results generated by impact analysis.

Contemporary enterprises depend to great extent on software systems. During the past decades the number of systems has been constantly increasing and these systems have become more integrated with one another. This has lead to a growing complexity in managing software systems and their environment. At the same time business environments today need to progress and change rapidly to keep up with evolving markets. As the business processes change, the systems need to be modified in order to continue supporting the processes. The complexity increase and growing demand for rapid change makes the management of enterprise systems a very important issue. In order to achieve effective and efficient management, it is essential to be able to analyze the system modifiability (i.e. estimate the future change cost). This is addressed in the thesis by employing architectural models. The contribution of this thesis is a method for software system modifiability analysis using enterprise architecture models. The contribution includes an enterprise architecture analysis formalism, a modifiability metamodel (i.e. a modeling language), and a method for creating metamodels. The proposed approach allows IT-decision makers to model and analyze change projects. By doing so, high-quality decision support regarding change project costs is received. This thesis is a composite thesis consisting of five papers and an introduction. Paper A evaluatesa number of analysis formalisms and proposes extended influence diagrams to be employed for enterprise architecture analysis. Paper B presents the first version of the modifiability metamodel. InPaper C, a method for creating enterprise architecture metamodels is proposed. This method aims to be general, i.e. can be employed for other IT-related quality analyses such as interoperability, security, and availability. The paper does however use modifiability as a running case. The second version of the modifiability metamodel for change project cost estimation is fully described in Paper D. Finally, Paper E validates the proposed method and metamodel by surveying 110 experts and studying 21 change projects at four large Nordic companies. The validation indicates that the method and metamodel are useful, contain the right set of elements and provide good estimation capabilities. / QC20100716