强化学习用于自动化程序修复中的突变操作员选择
Carol Hanna1, Aymeric Blot2, Justyna Petke1
1University College London, London, England, UK.
概括
用于自动程序修复突变操作员选择的强化学习产生了更多的通过测试的变体. 然而,与基于启发式修复的随机选择相比,它并没有显著改善修复错误率.
科学领域:
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
背景情况:
- 自动程序修复 (APR) 使用启发式搜索通过突变代码来修复软件错误.
- 当前的方法经常随机选择突变运算符,产生许多非编译或不正确的程序变体,浪费资源.
研究的目的:
- 调查强化学习 (RL) 在基于启发式的APR中选择突变运算符.
- 旨在减少无效程序变体的生成,提高修复效率.
主要方法:
- 开发了一种基于RL的方法来选择突变操作员,不依赖于编程语言,细节性和搜索策略.
- 在Defects4J的353个真实世界bug上进行了广泛的实证评估,使用了30080次修复尝试.
主要成果:
- 与基线随机选择相比,RL方法产生了更多的通过测试的变体.
- 在成功修补错误的数量上没有观察到显著的改善.
结论:
- 虽然RL增强了APR中有效变体的生成,但它还没有在提高错误修复率方面表现出明显的优势,而不是随机选择.
- 需要进一步的研究,以充分利用RL在优化基于启发式程序修复搜索策略的潜力.
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