SupConFL: Fault Localization with Supervised Contrastive Learning

Recent years have seen a growing interest in deep learning-based approaches to localize faults in software. However, existing methods have not reached a satisfying level of accuracy. The main reason is that the feature extraction of faulty code elements is insufficient. Namely, these deep learning-based methods will learn some features that are not relevant to fault localization, and thus ignore the features related to fault localization. We propose SupConFL, a new framework for statement-level fault localization. Our framework combines the statement-level abstract syntax tree with the statement sequence, and adopt controllable attention-based LSTM to locate the faulty elements. The training is done through contrastive learning between the faulty code and its fixed version. By comparing the faulty code with the fixed code, the model can learn richer features of the faulty code elements. Our experiments on Defects4j-1.2.0 dataset show that our method outperforms the current state-of-the-art. Specifically, SupConFL improves Top-1 score by 7.96% in comparison with the current state-of-the-art. In addition, our method has also achieved good results in cross-project experiments.