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计算机系统应用英文版:2023,32(12):261-267
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基于数据流分析的过拟合补丁识别
(中国石油大学(华东) 青岛软件学院、计算机科学与技术学院, 青岛 266580)
Overfitting Patch Identification Based on Data Flow Analysis
(Qingdao Institute of Software & College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China)
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Received:May 18, 2023    Revised:June 26, 2023
中文摘要: 自动程序修复技术可实现对软件缺陷的自动修复, 并使用测试套件评估修复补丁. 然而因为测试套件不充分, 通过测试套件的补丁可能并未正确修复缺陷, 甚至引入新的缺陷并产生波及效应, 导致自动程序修复生成大量过拟合补丁. 针对这个问题, 本文提出了一种基于数据流分析的过拟合补丁识别方法, 首先将补丁对程序的修改分解为对变量的操作, 然后采用数据流分析方法识别补丁影响域, 并根据补丁影响域选择针对性覆盖准则来识别目标覆盖元素, 进而选取测试路径并生成测试用例实现对修复程序的充分测试, 避免修复副作用的影响. 本文在两个数据集上进行了评估, 实验结果表明, 基于数据流分析的过拟合补丁识别方法可有效提升自动程序修复的正确性.
Abstract:Automatic program repair techniques can realize automatic repair of software defects and employ test suites to evaluate repair patches. However, because of inadequate test suites, the patches passing the test suites may not repair the defects correctly, or even introduce new defects with ripple effects, which results in a large number of overfitting patches generated by automatic program repair. To this end, an overfitting patch identification method based on data flow analysis is proposed. This method firstly decomposes the patch modifications to the program into operations on variables, then adopts data flow analysis to identify the patch influence domain, and selects targeted coverage criteria to identify target coverage elements according to the domain. Finally, test paths are selected and test cases are generated to fully test the repair program to avoid the impact of repairing side effects. This study conducts evaluations on two datasets, and the experimental results show that the overfitting patch identification method based on data flow analysis can improve the correctness of automatic program repair.
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基金项目:山东省自然科学基金(ZR2021MF058)
引用文本:
董玉坤,杨宇飞,程小彤,唐叶尔.基于数据流分析的过拟合补丁识别.计算机系统应用,2023,32(12):261-267
DONG Yu-Kun,YANG Yu-Fei,CHENG Xiao-Tong,TANG Ye-Er.Overfitting Patch Identification Based on Data Flow Analysis.COMPUTER SYSTEMS APPLICATIONS,2023,32(12):261-267