GrasP: Graph-to-Sequence Learning for Automated Program Repair

Tang, Ben Zhong; Li, Bin; Bo, Lili; Wu, Xiaoxue; Cao, Sicong; Sun, Xiaobing

doi:10.1109/qrs54544.2021.00091

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Tang et al [139] propose a novel end-to-end approach Grasp for repairing buggy Java programs. They represent the buggy method as a graph to retain structural information and apply the Graphto-Sequence model to capture information from the graph, overcoming the problem of information missing.…”

Section: Tree-based Generationmentioning

confidence: 99%

A Survey of Learning-based Automated Program Repair

Zhang¹,

Fang²,

Ma³

et al. 2023

Preprint

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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 opensource 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 https://github.com/QuanjunZhang/AwesomeLearningAPR. CCS Concepts: • Software and its engineering → Software testing and debugging; Software testing and debugging.

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Section: Tree-based Generationmentioning

confidence: 99%

A Survey of Learning-based Automated Program Repair

Zhang¹,

Fang²,

Ma³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Embedding Context as Code Dependencies for Neural Program Repair

Nashid

Sintaha

Mesbah

2023

2023 IEEE Conference on Software Testing, Verification and Validation (ICST)

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A Survey of Learning-based Automated Program Repair

Zhang,

Fang,

et al. 2023

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

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 article, 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 article 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 the repository: https://github.com/iSEngLab/AwesomeLearningAPR .

show abstract

GrasP: Graph-to-Sequence Learning for Automated Program Repair

Cited by 3 publications

References 19 publications

A Survey of Learning-based Automated Program Repair

A Survey of Learning-based Automated Program Repair

Embedding Context as Code Dependencies for Neural Program Repair

A Survey of Learning-based Automated Program Repair

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