A File-Statement Approach for Bug Localization: Optimizing IRBL and Combination Strategy
One of the main objectives of software testing is to locate the position of bugs. Bug localization is generally categorized into statement-level and file-level localization. File-level bug localization is typically performed using information retrieval-based bug localization (IRBL) methods. However,...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11028032/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849685312240877568 |
|---|---|
| author | Zhonghao Guo Xinyue Xu Xiangxian Chen Chenge Geng |
| author_facet | Zhonghao Guo Xinyue Xu Xiangxian Chen Chenge Geng |
| author_sort | Zhonghao Guo |
| collection | DOAJ |
| description | One of the main objectives of software testing is to locate the position of bugs. Bug localization is generally categorized into statement-level and file-level localization. File-level bug localization is typically performed using information retrieval-based bug localization (IRBL) methods. However, when used alone, file-level bug localization can only identify bugs at the file level and has low accuracy, limiting its practicality. Integrating file-level and statement-level bug localization can produce more precise results. To address these limitations, this study proposes a novel hierarchical bug localization framework that integrates multiple localization techniques across the file and statement levels. First, we present AS_IRBL, an enhanced IRBL method that introduces two innovations: a word-attention component that selectively amplifies the weight of key terms in bug reports, and a complex-word segmentation component that improves semantic matching by decomposing compound identifiers. These enhancements lead to significantly improved file-level localization performance. Second, we introduce C_FF_S, a new cross-level integration strategy that hierarchically combines file-level and statement-level localization results. Unlike prior approaches, C_FF_S uses an activation-based weighting mechanism to adjust statement-level suspicion scores according to file-level confidence, enabling context-aware and more accurate bug localization. Experimental results on the Defects4J benchmark demonstrate the effectiveness of our method: AS_IRBL improves MAP by 30.44% and Einspect@n by 31.89% over baseline IRBL. C_FF_S outperforms existing combination strategies, with MAP, MRR, and Einspect@n increased by 6.08%, 7.31%, and 7.58%, respectively. These results confirm the novelty and practical value of our hierarchical and mechanism-driven approach to multi-level bug localization. |
| format | Article |
| id | doaj-art-4b5e98abddee4c0e970d58840f7c97fb |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4b5e98abddee4c0e970d58840f7c97fb2025-08-20T03:23:11ZengIEEEIEEE Access2169-35362025-01-011310415910417210.1109/ACCESS.2025.357760811028032A File-Statement Approach for Bug Localization: Optimizing IRBL and Combination StrategyZhonghao Guo0https://orcid.org/0000-0001-7096-6722Xinyue Xu1https://orcid.org/0009-0004-6249-658XXiangxian Chen2Chenge Geng3College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, ChinaCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, ChinaCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, ChinaCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, ChinaOne of the main objectives of software testing is to locate the position of bugs. Bug localization is generally categorized into statement-level and file-level localization. File-level bug localization is typically performed using information retrieval-based bug localization (IRBL) methods. However, when used alone, file-level bug localization can only identify bugs at the file level and has low accuracy, limiting its practicality. Integrating file-level and statement-level bug localization can produce more precise results. To address these limitations, this study proposes a novel hierarchical bug localization framework that integrates multiple localization techniques across the file and statement levels. First, we present AS_IRBL, an enhanced IRBL method that introduces two innovations: a word-attention component that selectively amplifies the weight of key terms in bug reports, and a complex-word segmentation component that improves semantic matching by decomposing compound identifiers. These enhancements lead to significantly improved file-level localization performance. Second, we introduce C_FF_S, a new cross-level integration strategy that hierarchically combines file-level and statement-level localization results. Unlike prior approaches, C_FF_S uses an activation-based weighting mechanism to adjust statement-level suspicion scores according to file-level confidence, enabling context-aware and more accurate bug localization. Experimental results on the Defects4J benchmark demonstrate the effectiveness of our method: AS_IRBL improves MAP by 30.44% and Einspect@n by 31.89% over baseline IRBL. C_FF_S outperforms existing combination strategies, with MAP, MRR, and Einspect@n increased by 6.08%, 7.31%, and 7.58%, respectively. These results confirm the novelty and practical value of our hierarchical and mechanism-driven approach to multi-level bug localization.https://ieeexplore.ieee.org/document/11028032/Software testingbug localizationIRBLlearning to rank |
| spellingShingle | Zhonghao Guo Xinyue Xu Xiangxian Chen Chenge Geng A File-Statement Approach for Bug Localization: Optimizing IRBL and Combination Strategy IEEE Access Software testing bug localization IRBL learning to rank |
| title | A File-Statement Approach for Bug Localization: Optimizing IRBL and Combination Strategy |
| title_full | A File-Statement Approach for Bug Localization: Optimizing IRBL and Combination Strategy |
| title_fullStr | A File-Statement Approach for Bug Localization: Optimizing IRBL and Combination Strategy |
| title_full_unstemmed | A File-Statement Approach for Bug Localization: Optimizing IRBL and Combination Strategy |
| title_short | A File-Statement Approach for Bug Localization: Optimizing IRBL and Combination Strategy |
| title_sort | file statement approach for bug localization optimizing irbl and combination strategy |
| topic | Software testing bug localization IRBL learning to rank |
| url | https://ieeexplore.ieee.org/document/11028032/ |
| work_keys_str_mv | AT zhonghaoguo afilestatementapproachforbuglocalizationoptimizingirblandcombinationstrategy AT xinyuexu afilestatementapproachforbuglocalizationoptimizingirblandcombinationstrategy AT xiangxianchen afilestatementapproachforbuglocalizationoptimizingirblandcombinationstrategy AT chengegeng afilestatementapproachforbuglocalizationoptimizingirblandcombinationstrategy AT zhonghaoguo filestatementapproachforbuglocalizationoptimizingirblandcombinationstrategy AT xinyuexu filestatementapproachforbuglocalizationoptimizingirblandcombinationstrategy AT xiangxianchen filestatementapproachforbuglocalizationoptimizingirblandcombinationstrategy AT chengegeng filestatementapproachforbuglocalizationoptimizingirblandcombinationstrategy |