Evolutionary mutation testing

Context: Mutation testing is a testing technique that has been applied successfully to several programming languages. However, it is often regarded as computationally expensive, so several refinements have been proposed to reduce its cost. Moreover, WS-BPEL compositions are being widely adopted by developers, but present new challenges for testing, since they can take much longer to run than traditional programs of the same size. Therefore, it is interesting to reduce the number of mutants required. Objective: We present Evolutionary Mutation Testing (EMT), a novel mutant reduction technique for finding mutants that help derive new test cases that improve the quality of the initial test suite. It uses evolutionary algorithms to reduce the number of mutants that are generated and executed with respect to the exhaustive execution of all possible mutants, keeping as many difficult to kill and potentially equivalent mutants (strong mutants) as possible in the reduced set. Method: To evaluate EMT we have developed GAmera, a mutation testing system powered by a co-evolutive genetic algorithm. We have applied this system to three WS-BPEL compositions to estimate its effectiveness, comparing it with random selection. Results: The results obtained experimentally show that EMT can select all strong mutants generating 15% less mutants than random selection in over 20% less time for complex compositions. When generating a percentage of all mutants, EMT finds on average more strong mutants than random selection. This has been confirmed to be statistically significant within a 99.9% confidence interval. Conclusions: EMT has reduced for the three tested compositions the number of mutants required to select those which are useful to derive new test cases that improve the quality of the test suite. The directed search performed by EMT makes it more effective than random selection, especially as compositions become more complex and the search space widens.