TransRepair: Context-aware Program Repair for Compilation Errors

TL;DR

TransRepair is an innovative, context-aware neural network approach that automatically locates and repairs compilation errors in C programs, significantly improving repair accuracy by leveraging diagnostic feedback and extensive training data.

Contribution

It introduces a Transformer-based model that considers code context and diagnostic feedback, along with a large synthesized dataset for effective program repair.

Findings

Outperforms state-of-the-art in repair accuracy

Effectively utilizes diagnostic feedback and code context

Demonstrates robustness across diverse compilation errors

Abstract

Automatically fixing compilation errors can greatly raise the productivity of software development, by guiding the novice or AI programmers to write and debug code. Recently, learning-based program repair has gained extensive attention and became the state-of-the-art in practice. But it still leaves plenty of space for improvement. In this paper, we propose an end-to-end solution TransRepair to locate the error lines and create the correct substitute for a C program simultaneously. Superior to the counterpart, our approach takes into account the context of erroneous code and diagnostic compilation feedback. Then we devise a Transformer-based neural network to learn the ways of repair from the erroneous code as well as its context and the diagnostic feedback. To increase the effectiveness of TransRepair, we summarize 5 types and 74 fine-grained sub-types of compilations errors from two real-world program datasets and the Internet. Then a program corruption technique is developed to synthesize a large dataset with 1,821,275 erroneous C programs. Through the extensive experiments, we demonstrate that TransRepair outperforms the state-of-the-art in both single repair accuracy and full repair accuracy. Further analysis sheds light on the strengths and weaknesses in the contemporary solutions for future improvement.