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.


Evolutionary Testing


11th Annual Conference on Genetic and Evolutionary Computation, July 2009

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Year 2013 : 1 citations

 A Arcuri, G Fraser, 'Parameter tuning or default values? An empirical investigation in search-based software engineering', 2013. Link: Google Scholar ID: 4361730981190076156.