Feature selection in an interactive search-based PLA design approach

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an interactive optimization problem with many conflicting factors. Incorporate Decision Makers’ (DM) preferences during the search process may help the algorithms to find more adequate solutions for their profiles. Interactive approaches allow the DM to evaluate solutions, guiding the optimization according to their preferences. However, this brings up human fatigue problems caused by the excessive amount of interactions and solutions to evaluate. A common strategy to prevent this problem is limiting the number of interactions and solutions evaluated by the DM. Machine Learning (ML) models were also used to learn how to evaluate solutions according to the DM profile and replace them after some interactions. Feature selection performs an essential task as non-relevant and/or redundant features used to train the ML model can reduce the accuracy and comprehensibility of the hypotheses induced by ML algorithms. This work aims to select features of a ML model used to prevent human fatigue in an interactive search-based PLA design approach. We applied four selectors and through results we were able to reduce 30% of features, obtaining an accuracy of 99%.