KBL: a golden keywords-based query reformulation approach for bug localization

Reformulating initial bug reports to obtain better queries for buggy code retrieval is an important research direction in the bug localization area. Existing query reformulation strategies of bug reports are generally unsupervised and may lack localization guidance, which prevents the generation of...

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Veröffentlicht in:Empirical software engineering : an international journal Jg. 30; H. 5; S. 135
Hauptverfasser: Cai, Biyu, Zou, Weiqin, Meng, Qianshuang, Xu, Hui, Zhang, Jingxuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2025
Springer Nature B.V
Schlagworte:
Benchmarks
Compilers
Computer Science
Debugging
Feature extraction
Feedback
Genetic algorithms
Information retrieval
Interpreters
Keywords
Localization
Programming Languages
Queries
Query expansion
Relevance feedback
Retrieval performance measures
Software
Software Engineering/Programming and Operating Systems
Query reformulation
Bug report
Golden keywords
Bug localization
ISSN:1382-3256, 1573-7616
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Zusammenfassung:Reformulating initial bug reports to obtain better queries for buggy code retrieval is an important research direction in the bug localization area. Existing query reformulation strategies of bug reports are generally unsupervised and may lack localization guidance, which prevents the generation of better queries for bug localization. Towards this, we propose to develop KBL, a golden keywords-based query reformulation approach for bug localization. Specifically, we first leverage the genetic algorithm and keywords refinement heuristic rules to build a golden keywords benchmark targeted at bug localization. Taking this benchmark as bug localization guidance, we create a keywords classifier for bug reports based on three categories of semantic features. The extracted keywords by the classifier for a bug report are taken as the reformulated start point upon which noise removal and shared keyword expansion with historical bug reports are further performed. The final achieved query, as a replacement for the original bug report, is expected to enhance buggy code retrieval performance. Our experiments show that the contributed keywords benchmark is of high quality in locating bugs, establishing a good basis for further query reformulation to improve localization techniques. Through an analysis of different classifier choices, data balancing strategies, and feature importance, we validate the suitability of the configuration settings for our keyword classifier. A testing dataset of 4,484 bug reports from six projects is used to evaluate our KBL. The results show that KBL is found to substantially outperform both the typical (with a relatively 8%-85% higher Acc@10, 9%-93% higher MAP, and 10%-94% higher MRR), and state-of-the-art (with a relatively 21%-45% higher Acc@10, 31%-47% higher MAP and 32%-50% higher MRR) reformulation strategies. Moreover, based on the reformulated queries of our KBL, the performance of seven representative information retrieval-based bug localization techniques also showed recognizable improvements, including relative increases of 8%-36% in Acc@1, 6%-32% in Acc@5, 4%-24% in Acc@10, 4%-21% in Acc@20, 10%-33% in MAP, and 8%-25% in MRR.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1382-3256
1573-7616
DOI:10.1007/s10664-025-10694-2