Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix

TL;DR

This paper introduces a neural network-based approach for program repair that leverages a comprehensive representation of software programs, including syntax, data flow, and control flow, to improve bug fixing effectiveness.

Contribution

It proposes a unified representation capturing multiple aspects of software programs and a method to guide transformer models using this information for better bug repair.

Findings

More comprehensive program information improves ML-driven bug fixing.

Preliminary experiments show enhanced repair performance with richer data.

Unified representation aids in understanding complex program structures.

Abstract

The advance in machine learning (ML)-driven natural language process (NLP) points a promising direction for automatic bug fixing for software programs, as fixing a buggy program can be transformed to a translation task. While software programs contain much richer information than one-dimensional natural language documents, pioneering work on using ML-driven NLP techniques for automatic program repair only considered a limited set of such information. We hypothesize that more comprehensive information of software programs, if appropriately utilized, can improve the effectiveness of ML-driven NLP approaches in repairing software programs. As the first step towards proving this hypothesis, we propose a unified representation to capture the syntax, data flow, and control flow aspects of software programs, and devise a method to use such a representation to guide the transformer model from NLP in better understanding and fixing buggy programs. Our preliminary experiment confirms that the more comprehensive information of software programs used, the better ML-driven NLP techniques can perform in fixing bugs in these programs.