Generating Efficient Mutation Operators for Search-Based Model-Driven Engineering

Software engineers are frequently faced with tasks that can be expressed as optimization problems. To support them with automation, search-based model-driven engineering combines the abstraction power of models with the versatility of meta-heuristic search algorithms. While current approaches in this area use genetic algorithms with xed mutation operators to explore the solution space, the e ciency of these operators may heavily depend on the problem at hand. In this work, we propose FitnessStudio, a technique for generating e cient problem-tailored mutation operators automatically based on a two-tier framework. The lower tier is a regular meta-heuristic search whose mutation operator is trained by an upper-tier search using a higher-order model transformation. We implemented this framework using the Henshin transformation language and evaluated it in a benchmark case, where the generated mutation operators enabled an improvement to the state of the art in terms of result quality, without sacri cing performance.