Supporting user preferences in search-based product line architecture design using Machine Learning

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA design requires intensive human effort as it involves several conflicting factors. In order to support this task, an interactive search-based approach, automated by a tool named OPLA-Tool, was proposed in a previous work. Through this tool the software architect evaluates the generated solutions during the optimization process. Considering that evaluating PLA is a complex task and search-based algorithms demand a high number of generations, the evaluation of all solutions in all generations cause human fatigue. In this work, we incorporated in OPLA-Tool a Machine Learning (ML) model to represent the architect in some moments during the optimization process aiming to decrease the architect's effort. Through the execution of a quanti-qualitative exploratory study it was possible to demonstrate the reduction of the fatigue problem and that the solutions produced at the end of the process, in most cases, met the architect’s needs