Preserving Inter-gene Relations During Test Case Generation using Intelligent Evolutionary Operators

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Randomized variational operators can be very disruptive to the search process, especially when there exist dependencies between the variables under search. Within test-cases, these dependencies exist as well. This makes it interesting to evaluate the benefits of preserving these dependencies during test-case generation.

In this paper, we propose two variants of the Many-Objective Sorting Algorithm (MOSA). The first of which is based on Agglomerative Clustering, ACMOSA. The second is a Gene-pool Optimal Mixing based variant, GOMOSA. ACMOSA and GOMOSA model the inter-gene dependencies and use that model to intelligently perform crossover while preserving key building blocks within individuals. These novel techniques are evaluated in an empirical study and compared to MOSA and the Many Independent Objective algorithm (MIO). This study is composed of several benchmark RESTful APIs for which the algorithms generate test-cases.

The results of the empirical study show that, for 40% of the tested APIs, the novel techniques provide a significant benefit time-wise. For another 40% of the APIs, they perform equally well, and for 20% of the APIs under evaluation they performed worse.