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Automated program repair is a crucial task for improving the efficiency ofsoftware developers. Recently, neural-based techniques have demonstratedsignificant promise in generating correct patches for buggy code snippets.However, most existing approaches arbitrarily treat the buggy context withoutany analysis to capture the semantic relationship between the buggy statementand its context. Additionally, we observe that existing neural models mayoutput an unaltered patch consistent with the input buggy code snippet, whichfails to be the correct human-written one for fixing the given bug. To addressthe aforementioned limitations, we present in this paper a novel neural programrepair framework called \approach, which adapts the general pre-trainedlanguage model for fixing single-line Java bugs. We make the first attempt touse program slicing to extract contextual information directly related to thegiven buggy statement as repair ingredients from the corresponding programdependence graph and eliminate unaltered patches using an intuitive buteffective filter mechanism. We demonstrate the effectiveness of \approach onfive benchmarks when compared with state-of-the-art baselines.

Neural Program Repair with Program Dependence Analysis and Effective Filter Mechanism