A Survey of Learning-based Automated Program Repair

TL;DR

This survey reviews recent advances in learning-based automated program repair, emphasizing neural network approaches that treat bug fixing as a translation task, and discusses datasets, evaluation, and future directions.

Contribution

It provides a comprehensive overview of the state-of-the-art in learning-based APR, detailing workflows, components, datasets, evaluation metrics, and practical guidelines for future research.

Findings

Learning-based APR treats bug fixing as neural machine translation.

Remarkable performance achieved by deep learning techniques.

Discussion on datasets, evaluation, and industrial deployment challenges.

Abstract

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely-adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at \url{https://github.com/QuanjunZhang/AwesomeLearningAPR}.