一句话：提出首个多故障场景下补丁评估与过拟合的形式化框架，给出五种故障交互感知的评估策略分类。
补丁过拟合是自动程序修复（APR）中长期存在的难题，当验证仅依赖不完整的测试集时尤为突出。作者指出，多故障及其交互被普遍忽视，进一步加剧了该问题：传统 APR 工具孤立地处理各个故障，忽略了可能掩盖故障或引入回归的微妙交互。为此，本文提出五种「故障交互感知」的补丁评估策略分类法，并给出形式化模型，刻画每种策略应在何时、如何应用。该框架为稳健的多故障修复提供指导，揭示故障交互如何关键性地影响补丁评估结果。作者称这是首个针对多故障场景下补丁评估与过拟合的形式化处理，为更可靠、更实用的 APR 奠定基础。
Patch overfitting remains a persistent challenge in automated program repair (APR), especially when validation depends on incomplete test suites. We argue that this problem is significantly exacerbated by the overlooked presence of multiple interacting faults, a common yet under-addressed reality in real-world software. Conventional APR tools typically treat faults in isolation, neglecting subtle interactions that can mask faults or introduce regressions. To address this, we develop a taxonomy of five fault interaction-aware patch assessment strategies, supported by a formal model that identifies when and how each should be applied. Our framework guides robust multi-fault repair and exposes how fault interactions critically influence patch outcomes. To our knowledge, this is the first formal treatment of patch assessment and overfitting in multi-fault settings, offering a foundation for more reliable and practical APR.
DOI 原文 ·
@inproceedings{paperbot9234,
title = {Interaction-Aware Patch Assessment for Multi-Fault Automated Program Repair},
author = {Omar I. Al-Bataineh},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00319}
}