An Method of Intelligent Duplicate Bug Report Detection Based on Technical Term Extraction
As the bug description data generated during the software maintenance cycle, bug reports are usually hastily written by different users, resulting in many redundant and duplicate bug reports (DBRs). Once the DBRs are repeatedly assigned to developers, it will inevitably lead to a serious waste of human resources, especially for large-scale open-source projects. Recently, many experts and scholars have devoted themselves to researching the detection of DBRs and put forward a series of detection methods for DBRs. However, there is still much room for improvement in the performance of DBR prediction. Therefore, this paper proposes a new method for detecting DBR based on technical term extraction, CTEDB (Combination of Term Extraction and DeBERTaV3) for short. This method first extracts technical terms from the text information of bug reports based on Word2Vec and TextRank algorithms. Then it calculates the semantic similarity of technical terms between different bug reports by combining Word2Vec and SBERT models. Finally, it completes the DBR detection task by combining the DeBERTaV3 model. The experimental results show that CTEDB has achieved good results in detecting DBR, and has obviously improved the accuracy, F1-score, recall and precision compared with the baseline approaches.
Mon 15 MayDisplayed time zone: Hobart change
11:00 - 12:30 | |||
11:00 22mTalk | An Method of Intelligent Duplicate Bug Report Detection Based on Technical Term Extraction AST 2023 Xiaoxue Wu Yangzhou University, Wenjing Shan Yangzhou University, Wei Zheng Northwestern Polytechnical University, Zhiguo Chen Northwestern Polytechnical University, Tao Ren Yangzhou University, Xiaobing Sun Yangzhou University | ||
11:22 22mTalk | A Reinforcement Learning Approach to Generate Test Cases for Web Applications AST 2023 Xiaoning Chang Institute of Software, Chinese Academy of Sciences, Zheheng Liang Joint Laboratory on Cyberspace Security of China Southern Power Grid, Yifei Zhang State Key Lab of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Lei Cui Joint Laboratory on Cyberspace Security of China Southern Power Grid, Zhenyue Long , Guoquan Wu Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Chinese Academy of Sciences Nanjing College; China Southern Power Grid, Yu Gao Institute of Software, Chinese Academy of Sciences, China, Wei Chen Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Chinese Academy of Sciences Nanjing College, Jun Wei Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Chinese Academy of Sciences Chongqing School, Tao Huang Institute of Software Chinese Academy of Sciences | ||
11:45 22mTalk | Cross-Project setting using Deep learning Architectures in Just-In-Time Software Fault Prediction: An Investigation AST 2023 Sushant Kumar Pandey Chalmers and University of Gothenburg, Anil Kumar Tripathi Indian Institute of Technology (BHU), Varanasi | ||
12:07 22mTalk | On Comparing Mutation Testing Tools through Learning-based Mutant Selection AST 2023 Milos Ojdanic University of Luxembourg, Ahmed Khanfir University of Luxembourg, Aayush Garg University of Luxembourg, Luxembourg, Renzo Degiovanni SnT, University of Luxembourg, Mike Papadakis University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg File Attached | ||