Year: 2025
Type: article
Abstract: A large number of bug reports are submitted by users and developers in bug-tracking system every day. It is time-consuming for software maintainers to assign bug reports to appropriate developers for fixing manually. Many bug-triaging methods have been developed to automate this process. However, most previous studies mainly focused on analyzing textual content and failed to make full use of the structured information embedded in the bug-tracking system. In fact, this structured information, which plays an important role in bug triaging, reflects the process of bug tracking and the historical activities. To further improve the performance of automatic bug triaging, in this study, we propose a new representation learning model, PTITransE, for knowledge bases, which extends TransE via enhancing the embeddings with textual entity descriptions and is more suitable for bug triaging. Moreover, we make the first attempt to apply knowledge base and link prediction techniques to bug triaging. For each new bug report, the proposed framework can recommend top-k developers for fixing the bug report by using the learned embeddings of entities and relations. Evaluation is performed on three real-world projects, and the results indicate that our method outperforms baseline bug triaging approaches and can alleviate the cold-start problem in bug triaging.
DOI: 10.3390/make7020057
Authors: Qi Wang, Weihao Yan, Yanlong Li, Yizheng Ge, Yiwei Liu, Peng Yin, Shuai Tan
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4411456270