Institutional Repository
| mining associations to improve the effectiveness of fault localization | |
| Zhao Lei; Wang Li-Na; Gao Dong-Ming; Zhang Zhen-Yu; Xiong Zuo-Ting | |
| 2012 | |
| Source | Jisuanji Xuebao/Chinese Journal of Computers
 |
| ISSN | 0254-4164 |
| Volume | 35Issue:12Pages:2528-2540 |
| English Abstract | Coverage-based fault localization (CBFL) techniques find the fault-related positions in programs by comparing the execution statistics of passed executions and failed executions have been proven to be efficient by several empirical studies. However, these techniques assess the suspiciousness of program entities individually, whereas the individual coverage information cannot reflect the complicated control- and data-dependency relationships, and thus oversimplify the execution spectra. In this paper, we first use motivating examples to show the impact of the cause-effect relationship on the effectiveness of CBFL. Second, we propose the rules of program failures and design the execution analysis model based on the coverage of different program execution spectrum. By computing the frequency items for statements with high suspiciousness, we also bring out the coverage vector to mine fault-prone statements. The controlled experiments based on the SIR benchmarks indicate that our technique is promising.; Coverage-based fault localization (CBFL) techniques find the fault-related positions in programs by comparing the execution statistics of passed executions and failed executions have been proven to be efficient by several empirical studies. However, these techniques assess the suspiciousness of program entities individually, whereas the individual coverage information cannot reflect the complicated control- and data-dependency relationships, and thus oversimplify the execution spectra. In this paper, we first use motivating examples to show the impact of the cause-effect relationship on the effectiveness of CBFL. Second, we propose the rules of program failures and design the execution analysis model based on the coverage of different program execution spectrum. By computing the frequency items for statements with high suspiciousness, we also bring out the coverage vector to mine fault-prone statements. The controlled experiments based on the SIR benchmarks indicate that our technique is promising. |
| Indexed Type | EI |
| Keyword | Hardware Software Engineering |
| Department | (1) Key Laboratory of Aerospace Information Security and Trust Computing Ministry of Education Wuhan 430072 China; (2) School of Computer Wuhan University Wuhan 430072 China; (3) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China |
| Language | 中文 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15330 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Zhao Lei,Wang Li-Na,Gao Dong-Ming,et al. mining associations to improve the effectiveness of fault localization[J]. Jisuanji Xuebao/Chinese Journal of Computers,2012,35(12):2528-2540. |
| APA | Zhao Lei,Wang Li-Na,Gao Dong-Ming,Zhang Zhen-Yu,&Xiong Zuo-Ting.(2012).mining associations to improve the effectiveness of fault localization.Jisuanji Xuebao/Chinese Journal of Computers,35(12),2528-2540. |
| MLA | Zhao Lei,et al."mining associations to improve the effectiveness of fault localization".Jisuanji Xuebao/Chinese Journal of Computers 35.12(2012):2528-2540. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment