An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied to solve hard real software engineering problems. However, choose and design a MOEA is considered difficult task, since there are several parameters components be configured. These aspects directly impact the generated solutions performance of MOEAs. In this sense, paper proposes an approach for automatic generation MOEAs Integration Test Order (ITO) problem. Such problem refers optimal sequences units integration testing. The includes set different MOEAs, implemented with two algorithms: Grammatical Evolution (GE) Iterated Racing (irace). Evaluation results presented, comparing by both algorithms. Furthermore, compared well-known used in literature ITO Results show that GE irace perform similarly, outperform traditional can reduce efforts spent configure serves as basis implementing other