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arXiv · 2501.18820

SoK: Towards Effective Automated Vulnerability Repair

Ying Li·Faysal hossain shezan·Bomin wei·Gang Wang·Yuan Tian
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Abstract

The increasing prevalence of software vulnerabilities necessitates automated vulnerability repair (AVR) techniques. This Systematization of Knowledge (SoK) provides a comprehensive overview of the AVR landscape, encompassing both synthetic and real-world vulnerabilities. Through a systematic literature review and quantitative benchmarking across diverse datasets, methods, and strategies, we establish a taxonomy of existing AVR methodologies, categorizing them into template-guided, search-based, constraint-based, and learning-driven approaches. We evaluate the strengths and limitations of these approaches, highlighting common challenges and practical implications. Our comprehensive analysis of existing AVR methods reveals a diverse landscape with no single ``best'' approach. Learning-based methods excel in specific scenarios but lack complete program understanding, and both learning and non-learning methods face challenges with complex vulnerabilities. Additionally, we identify emerging trends and propose future research directions to advance the field of AVR. This SoK serves as a valuable resource for researchers and practitioners, offering a structured understanding of the current state-of-the-art and guiding future research and development in this critical domain.

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Ying Li, Faysal hossain shezan, Bomin wei, Gang Wang, Yuan Tian. 2025-01-31. SoK: Towards Effective Automated Vulnerability Repair. https://arxiv.org/abs/2501.18820

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