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Software Fault Localization refers to the activity of finding code elements(e.g., statements) that are related to a software failure. The state-of-the-artfault localization techniques, however, produce coarse-grained results that candeter manual debugging or mislead automated repair tools. In this work, wefocus specifically on the fine-grained identification of code elements (i.e.,tokens) that must be changed to fix a buggy program: we refer to it as fixlocalization. This paper introduces a neural network architecture (named Beep)that builds on AST paths to predict the buggy code element as well as thechange action that must be applied to repair a program. Leveraging massive dataof bugs and patches within the CoCoNut dataset, we trained a model that was (1)effective in localizing the buggy tokens with the Mean First Rank significantlyhigher than a statistics based baseline and a machine learning-based baseline,and (2) effective in predicting the repair operators (with the associated buggycode elements) with a Recall@1= 30-45% and the Mean First Rank=7-12 (evaluatedby CoCoNut, ManySStuBs4J, and Defects4J datasets). To showcase how fine-grainedfix localization can help program repair, we employ it in two repair pipelineswhere we use either a code completion engine to predict the correct token or aset of heuristics to search for the suitable donor code. A key strength ofaccurate fix localization for program repair is that it reduces the chance ofpatch overfitting, a challenge in generate-and-validate automated programrepair: both two repair pipelines achieve a correctness ratio of 100%, i.e.,all generated patches are found to be correct. Moreover, accurate fixlocalization helps enhance the efficiency of program repair.

Beep: Fine-grained Fix Localization by Learning to Predict Buggy Code Elements