Bug Report Severity Prediction Based on Fine-tuned Embedding Model with Domain Knowledge
Accurately predicting the severity of bug reports is crucial for efficiently assigning them and facilitating developers to timely detect and fix software bugs.However,existing severity prediction methods based on traditional information retrieval or general pre-training models have limitations in prediction accuracy due to the ignorance of context semantics or bug report charac-teristics.To address this problem,this paper proposes a severity prediction method based on domain knowledge fine-tuning.A BERT pre-trained model that can fully consider the semantic context of text is used,and the model is fine-tuned with bug report data to learn relevant domain knowledge.The fine-tuned BERT model is then used to extract semantic features of bug reports,and a support vector machine is employed to construct a severity prediction model.Experimental results on 15 projects,including Mozilla,Eclipse,and Apache,demonstrate that compared with traditional information retrieval methods,the proposed method can improve the accuracy,recall,and F1 score by 4.5％to 22.0％,3.0％to 22.0％,and 4.0％to 22.0％,respectively.Compared with the general BERT model,the fine-tuned BERT model can improve the accuracy,recall,and F1 score by 2.0％～5.1％,1.9％～5.1％,and 1.8％～5.0％,respectively.
Word embeddingBERTPretrained modelBug reportFine-tuningSeverity prediction
NETL
NSTL
万方数据
