Publications
Cross-project bug type prediction based on transfer learning, Xiaoting Du, Zenghui Zhou, Beibei Yin, Guanping Xiao.
- Proposed a framework of cross-project bug type prediction based on a transfer learning method, TrAdaBoost.
- Conduct experimrents on four projects Linux, MySQL, HTTPD, and AXIS which show that the framework can improve the results of cross-project bug type prediction.
- The impact factors of the prediction results were investigated, including the pair of source and target projects, and the data size of the source project.
DEEPSIM: Deep Semantic Information-Based Automatic Mandelbug Classification, Xiaoting Du , Zheng Zheng, Guanping Xiao, Zenghui Zhou, Kishor S. Trivedi.
- Proposed DEEPSIM, an automatic Mandel- bug classification method that combines a semantic model with a deep learning classifier.
- We evaluated DEEPSIM on a total of 6557 bug reports and compared the results with those of existing studies.The experimental results showed that our method was effective for the classification of Mandel- bugs and performed better than the existing methods for this purpose.
- We investigated the impacts of the semantic model and word embedding parameters on the classification results.t.
KBS 2024TraceNet: Tracing and locating the key elements in sentiment analysis, Qinghua Zhao, Junfeng Liu, Zhongfeng Kang, Zenghui Zhou. Project
ESA 2023Is word order considered by foundation models? A comparative task-oriented analysis, Qinghua Zhao, Jiaang Li, Junfeng Liu, Zhongfeng Kang, Zenghui Zhou. Project
MET 2021Follow-up Test Cases are Better Than Source Test Cases in Metamorphic Testing: A Preliminary Study, Zenghui Zhou, Zheng Zheng, Tsong Yueh Chen, Jinyi Zhou,Kun Qiu. Project