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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：SCIENCE CHINA-INFORMATION SCIENCES
关键字：fault context fault localization program spectrum absolute rank debugging
摘要：Lightweight fault localization (LFL), which outputs a list of suspicious program entities in descending order based on their likelihood to be a root fault, is a popular method used by programmers to assist them in debugging. However, owing to the nature of program structures, it is impossible for LFL to always rank the root faulty program entity at the top of a ranking list. Recently, Xia et al. noted in their study that programmers inspect the first top-K-ranked program entities outputted by an LFL tool in sequence. Therefore, it is of practical significance to further improve the absolute rank of those buggy programs by using LFL if the root fault is ranked higher. To solve this issue, we propose a new LFL combined with a fault context to improve the fault absolute rank. We conduct experiments in which we apply our proposed approach to seven Siemens benchmark programs. The results show that by combining the suspiciousness scores of program entities with their fault-context suspiciousness scores that are based on an LFL called Dstar, our approach can improve the fault absolute rank with an effectiveness rate of 35.7% for 129 faulty versions from the seven benchmark programs. It should be noted that our approach can obtain an average improvement of 65.18% for those improved programs to which LFL can be effectively applied, and that there were improvements to seven top-ranked root faults of buggy programs.
ISSN号：1674-733X
是否译文：否
发表时间：2017-09-01
合写作者：黄志球,李勇,Fang, Bingwu
通讯作者：王永亮