Automatic Test Case Generation Based on Monte Carlo Tree Search Algorithm
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IJERTV11IS030064
Automatic Test Case Generation Based on Monte Carlo Tree Search Algorithm
Angwech Kevin , Said Kabir Sulaiman
Automatic test case generation is an important component of software debugging process. However, maximizing coverage is still a major problem in software testing. This paper proposed Monte Carlo Tree Search (MCTS) for automatic test case generation. The proposed MCTS Test Case Generation Method (M-TCG) maximizes branch coverage of derived test cases within the available test resources. M-TCG method formulates test case generation problem as a tree exploration problem and then uses MCTS to heuristically search optimal test cases. To evaluate the effectiveness and efficiency of our method, comparative experiments were conducted based on 17 projects with 66 non trivial Java classes from SF110 corpus and other benchmarks. Experiment results indicate that the proposed method improved average branch coverage by 2.1% compared with DynaMOSA, a state-of-the-art evolutionary technique. Our method also decreased memory consumption by 4% compared with the existing adaptive aDynaMOSA approach.
