Generating repairs for inconsistent models.

There are many repair alternatives for resolving model inconsistencies, each involving one or more model changes. Enumerating them all could overwhelm the developer because the number of possible repairs can grow exponentially. To address this problem, this paper focuses on the immediate cause of an inconsistency. By focusing on the cause, we can generate a repair tree with a subset of repair actions focusing on fixing this cause. This strategy identifies model elements that must be repaired, as opposed to additional model elements that may or may not have to be repaired later. Furthermore, our approach can provide an ownership-based filter for filtering repairs that modify model elements not owned by a developer. This filtering can further reduce the repair possibilities, aiding the developer when choosing repairs to be performed. We evaluated our approach on 24 UML models and four Java systems, using 17 UML consistency rules and 14 Java consistency rules. The evaluation data contained 39,683 inconsistencies, showing our approach's usability as the repair trees sizes ranged from five to nine on average per model. Also, these repair trees were generated in 0.3 seconds on average, showing our approach's scalability. Based on the results, we discuss the correctness and minimalism with regard to the cause of the inconsistency. Lastly, we evaluated the filtering mechanism, showing that it is possible to further reduce the number of repairs generated by focusing on ownership.

Intensifying the search-based optimization of product line architectures with crossover operators.

The Product Line Architecture (PLA) is a crucial artifact for the development of Software Product Lines. However, PLA is a complex artifact to be designed due to its large size and the multiple conflicting properties that need to be considered to ensure its quality, requiring a great effort for the architect. PLA designing has been formulated as an optimization problem aiming at improving some architectural properties in order to maximize both the feature modularization and the relational cohesion, and to minimize the class coupling. This kind of problem was successfully solved by multi-objective evolutionary algorithm. Nevertheless, most of existing approaches optimize PLA designs without applying the crossover operator, one of the fundamental genetic operators. To overcome these limitations, this paper aims to intensify the search-based PLA design optimization by presenting three crossover operators. These operators were empirically evaluated in quantitative and qualitative studies using three well-studied PLA designs. The experiments were conducted with eight experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results showed that there are significant differences among the use of only mutation and mutation with crossover. Also, we observed that the crossover operators contributed to generate solutions with better feature modularization. Finally, we could see that the proposed operators complement each other, since the experiment that combines at least two of the proposed operators achieved better results.