An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing

This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm's efficiency considerably, while introducing a negligible computational overhead.