Promovendo modularidade em um processo de Engenharia de Requisitos para linhas de produto de software - Silva Netto, Dorgival Pereira da (2015)
Goal Oriented Requirements Engineering approaches capture both the stakeholders’ goals and the requirements of the system-to-be, so that the latter corresponds to the stakeholders desires. Goal models can capture similarities and the variability of a Software Product Line (SPL), but they cannot describe the detailed behavior of its functionality. Due to this limitation, a process called GS2SPL (Goals and Scenarios to Software Product Lines) was defined to systematically obtain, from goal models, feature models and the specification of use case scenarios with variability described in PLUSS (Product Line Use case modeling for Systems and Software engineering). However, the variability of the SPL and the configuration knowledge are tangled an the scenarios described in PLUSS, jeopardizing the maintenance and reuse of artifacts. In order to solve this problem, it was proposed techniques to specific use case scenarios with separation of crosscutting concerns (or just, aspectual scenarios). One of these techniques is called MSVCM (Modeling Scenario Variability as Crosscutting Mechanisms), which specifies the variability and configuration knowledge of a SPL separately, as well as it defines a process to configure the specifications of a product. Thus, this work proposes an extension of the GS2SPL to obtain, systematically, a feature model and a specification of aspectual scenarios in MSVCM, from goal models. This approach is called GAS2SPL (Goals and Aspectual Scenarios to Software Product Lines) and their activities were described using the TaRGeT (Test and Requirements Generation Tool) example. GAS2SPL approach was evaluated through a comparative study between TaRGeT and MyCourses artifacts generated by GS2SPL and GAS2SPL approaches, taking into account modularity (features scattering and tangling scenarios) and expressiveness (how detailed are the configuration knowledge). After evaluating our approach, we realize that GAS2SPL approach reduced in the features scattering and tangling in the scenarios to zero, addition to own a knowledge configuration more specific because uses less symbols for it elaborate.