Test Data Generation for MC/DC Criterion using Reinforcement Learning

Ján Cegin, Karol Rástocný

Abstract: Unit testing focused on MC/DC criterion is essential in development of safety-critical systems. However design of test data that meet the MC/DC criterion needs detailed manual analysis of branching in units under test by test engineers. To deal with this problem we propose a new test data generation approach based on reinforcement learning, which utilize analogy with a game, in which a gamer, the test engineer, plays in an environment, a unit under test, and tries to achieve the highest possible reward, MC/DC coverage. We evaluated our approach for two different granularity levels, test suite and test case, and for two different action types allowed to the gamer, discrete and continuous action spaces. Preliminary results shows that the proposed approach could solve path explosion problem of symbolic approaches and that the proposed approach achieves at least comparable results to the current state-of-the-art search-based test data generation approaches.