Miguel Goulão

Prediction models of software change requests are useful for supporting rational and timely resource
allocation to the evolution process. In this paper we use a time series forecasting model to predict software
maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates
effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data.

Scenario modeling can be realized through different perspectives. In UML, scenarios are often modeled with activity models, in an early stage of development. Later, sequence diagrams are used to detail object interactions. The migration from activity diagrams to sequence diagrams is a repetitive and error-prone task. Model-Driven Development (MDD) can help streamlining this process, through transformation rules. Since the information in the activity model is insufficient to generate the corresponding complete sequence model, manual refinements are required. Our goal is to compare the relative effort of building the sequence diagrams manually with that of building them semi-automatically. Our results show a decrease in the number of operations required to build and refine the sequence model of approximately 64% when using MDD, when compared to the manual approach.

Software reuse is a broadly accepted practice to improve software development quality and productivity. Although an object of study in software engineering since the late sixties, achieving effective reuse remains challenging for many software development organizations. This paper reports a systematic mapping study on how reusability relates to other non-functional requirements and how different contextual factors influence the success of a reuse initiative. The conclusion is that the relationships are discussed rather informally, and that human, organizational, and technological domain factors are extremely relevant to a particular reuse context. This mapping study highlights the need for further research to better understand how exactly the different non-functional requirements and context factors affect reusability.

There are several tools currently available in the i * community. These tools have different features and purposes. Choosing the most adequate tool for a specific modelling situation can be a challenge. To overcome this diffculty, we present a systematic comparison of the i * tools listed in the i * wiki page, according to their features, syntax coverage and semantic analysis support. Our comparison highlights the
different strengths of those tools, to help identifying situations for which each tool might be particularly useful. We contribute with an aggregated vision of current i * tool support to the body of knowledge of the i * community. In addition, this comparison also helps identifying opportunities for further evolution of the surveyed tools.