面向自动修复并融合失效场景的缺陷定位方法

Fault Localization Using Failure-related Contexts for Automatic Program Repair

为了应对日益增长的软件修复开销,研究高效的软件自动修复技术成为学术界和工业界的共识。缺陷定位作为自动修复技术的前端,是实现快速准确自动修复的关键,其精度直接影响自动修复的性能。然而,初步研究表明,现有缺陷定位技术缺乏对自修复需求的考虑,对自修复算法支持有限。有必要研究面向自修复的高精度自动化缺陷定位技术,以提升自修复性能。因此,提出了失效场景的缺陷定位方法来应对该问题。提出的方法首先采用程序切片技术,构造出与失效相关的场景;然后对失效场景的各个元素实施可疑值度量;最后将可疑值度量化的场景交给自动修复技术实施修复。初步实验结果表明,本缺陷定位方法能有效提升自动修复性能。

In face of the high cost consumed by program repair in the software life-cycle, researchers from both academia and industry are trying to develop an effective automatic program repair technique to address this problem. Being an essential part of program repair, fault localization plays an important role in repair performance. However, preliminary studies have shown that existing fault localization approaches do not take into account the features of automatic repair, and therefore restrict repair performance. It is vital to design fault localization approaches for automatic repair to improve its performance. To address this issue, this paper proposed a fault localization approach using failure-related contexts for improving automatic program repair. The proposed approach first utilizes program slicing technique to construct a failure-related context, then evaluates the suspiciousness of each element in the context of being faulty, and finally presents this context and its elements with different suspiciousness to automatic program repair techniques for performing repair on faulty programs. The preliminary experimental results demonstrate that the proposed approach is effective to improve automatic repair performance.