Evolving Paradigms in Automated Program Repair: Taxonomy, Challenges, and Opportunities
Authors: 
Kai Huang, Zhengzi Xu, Su Yang, Hongyu Sun, Xuejun Li, 
Zheng Yan, Yuqing ZhangAuthors Info & Claims
Kai Huang, Zhengzi Xu, Su Yang, Hongyu Sun, Xuejun Li, Zheng Yan, Yuqing ZhangAuthors Info & ClaimsArticle No.: 36, Pages 1 - 43
Abstract
With the rapid development and large-scale popularity of program software, modern society increasingly relies on software systems. However, the problems exposed by software have also come to the fore. The software bug has become an important factor troubling developers. In this context, Automated Program Repair (APR) techniques have emerged, aiming to automatically fix software bug problems and reduce manual debugging work. In particular, benefiting from the advances in deep learning, numerous learning-based APR techniques have emerged in recent years, which also bring new opportunities for APR research. To give researchers a quick overview of APR techniques’ complete development and future opportunities, we review the evolution of APR techniques and discuss in depth the latest advances in APR research. In this article, the development of APR techniques is introduced in terms of four different patch generation schemes: search-based, constraint-based, template-based, and learning-based. Moreover, we propose a uniform set of criteria to review and compare each APR tool and then discuss the current state of APR development. Finally, we analyze current challenges and future directions, especially highlighting the critical opportunities that large language models bring to APR research.
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Index Terms
- Evolving Paradigms in Automated Program Repair: Taxonomy, Challenges, and Opportunities
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February 2025
974 pages
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 October 2024
Online AM: 19 September 2024
Accepted: 16 September 2024
Revised: 24 August 2024
Received: 05 August 2022
Published in CSUR Volume 57, Issue 2
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