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Automatic software fault localization serves as a significant purpose in helping developers solve bugs efficiently. Existing approaches for software fault localization can be categorized into static methods and dynamic ones, which have improved the fault locating ability greatly by analyzing static features from the source code or tracking dynamic behaviors during the runtime respectively. However, the accuracy of fault localization is still unsatisfactory.

To enhance the capability of detecting software faults with the statement granularity, this paper puts forward ALBFL, a novel neural ranking model that combines the static and dynamic features, which obtains excellent fault localization accuracy. Firstly, ALBFL learns the semantic features of the source code by a transformer encoder. Then, it exploits a self-attention layer to integrate those static features and dynamic features. Finally, those integrated …