ZHOU Wenjie
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610041, P. R. ChinaXIE Qi
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610041, P. R. ChinaCUI Mengtian
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610041, P. R. ChinaThe Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610041, P. R. China
TP311.5
Supported by National Natural Science Foundation of China(61502401, 12050410248), Sichuan Science and Technology Program(2021YFH0120), and Fundamental Research Funds for the Central Universities, Southwest Minzu University (2020YYXS59).
A duplicate bug report detection model with enhanced text relevance semantics and multi-feature extraction was proposed to address the issues of semantic long-distance dependence and the singleness of bug report features in the current research on duplicate bug report detection. The model introduced the self-attention mechanism to capture the semantic relevance within the bug report text sequence. This mechanism calculates the contextual semantic vector dynamically for semantic analysis and resolves the problem of long-distance dependence. Additionally, the model employed the latent Dirichlet allocation algorithm to capture the topic characteristics of the bug report text. Furthermore, a feature extraction network was constructed to calculate category difference features, providing category information for the bug report simultaneously. Finally, comprehensive detection was performed based on three types of feature vectors. The experimental results demonstrate that the model achieves improved detection performance.
周文杰,谢琪,崔梦天.强化文本关联语义和多特征提取的重复缺陷报告检测模型[J].重庆大学学报,2023,46(7):53~62
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