Empowering the Human as the Fitness Function in Search-Based Model-Driven Engineering

In Search-Based Software Engineering, more than 100 works have involved the human in the search process to obtain better results. However, the case where the human completely replaces the fitness function remains neglected. There is a good reason for that; no matter how intelligent the human is, humans cannot assess millions of candidate solutions as heuristics do. In this work, we study the influence of using the Human as the Fitness Function (HaFF) on the quality of the results. To do that, we focus on Search-Based Model-Driven Engineering (SBMDE) because inspecting models should require less human effort than inspecting code thanks to the abstraction of models. Therefore, we analyze the impact of HaFF in a real-world industrial case study of feature location in models. Furthermore, we also consider a reformulation operation (replacement) in the evaluation because a recent work reported that this operation significantly reduces the number of iterations required in comparison to the widespread crossover and mutation operations. The combination of HaFF and the reformulation operation (HaFF_R) improves the results of the best baseline by 0.15% in recall and 14.26% in precision. Analyzing the results, we learned how to better leverage HaFF_R, which increased the improvement with regard to the best baseline to 1.15% in recall and 20.05% in precision. HaFF_R significantly improves precision because humans are immune to the main limitations of the baselines: vocabulary mismatch and tacit knowledge. A focus group confirmed the acceptance of HaFF. These results are relevant for SBMDE because feature location is one of the main activities performed during maintenance and evolution. Our results, and what we learned from them, can also motivate and help other researchers to explore the benefits of HaFF. In fact, we provide a guideline that further discusses how to apply HaFF to other software engineering problems.