An Analysis of the Automatic Bug Fixing Performance of ChatGPT

TL;DR

This paper evaluates ChatGPT's ability to automatically fix software bugs, demonstrating its competitive performance and potential for improvement through interactive hints, surpassing existing deep learning and traditional methods.

Contribution

It provides the first comprehensive assessment of ChatGPT's bug fixing capabilities and highlights its advantages over prior approaches, including interactive hint integration.

Findings

ChatGPT fixes 31 out of 40 bugs with hints.

Performance is comparable to or better than other deep learning models.

Interactive hints significantly improve bug fixing success.

Abstract

To support software developers in finding and fixing software bugs, several automated program repair techniques have been introduced. Given a test suite, standard methods usually either synthesize a repair, or navigate a search space of software edits to find test-suite passing variants. Recent program repair methods are based on deep learning approaches. One of these novel methods, which is not primarily intended for automated program repair, but is still suitable for it, is ChatGPT. The bug fixing performance of ChatGPT, however, is so far unclear. Therefore, in this paper we evaluate ChatGPT on the standard bug fixing benchmark set, QuixBugs, and compare the performance with the results of several other approaches reported in the literature. We find that ChatGPT's bug fixing performance is competitive to the common deep learning approaches CoCoNut and Codex and notably better than the results reported for the standard program repair approaches. In contrast to previous approaches, ChatGPT offers a dialogue system through which further information, e.g., the expected output for a certain input or an observed error message, can be entered. By providing such hints to ChatGPT, its success rate can be further increased, fixing 31 out of 40 bugs, outperforming state-of-the-art.