Simultaneous Localization of Multiple Defects in Software Testing Based on Reinforcement Learning

At present, most software defect localization methods focus on single defect localization, but few on multi-defect localization. Therefore, the multi-defect localization method based on reinforcement learning is proposed. By using genetic algorithm, the candidate distribution population can be transformed into a sort of suspicious value of real program entity, and the location of multiple defects in software testing can be realized simultaneously. Experimental results show that, compared with the average evaluation index of the existing methods, the evaluation index \(EXAM_{F}\) of the proposed method is reduced by 1.19 and \(EXAM_{L}\) reduced by 1.05, which shows that the proposed method has better positioning performance and is suitable for popularization.

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