LAUSR

The GitHub issue community serves as the primary means for project developers to obtain information about program bugs, and numerous GitHub users post issues based on encountered project vulnerabilities or error messages. However, these issues often vary in quality, leading to a significant time burden on project developers. By collecting 2500 bug‐related issues from five GitHub projects, we first manually analyze a large volume of issue information to formulate rules for identifying whether a bug‐tagged issue is truly fixed by project developers. We find that a substantial number (ranging from 29% to 68.4% in different projects) of bug‐tagged issues are not truly fixed by project developers. We empirically investigate the characteristics of such issues and summarize the reasons why they are not fixed. Then, we propose an automated approach called DFBERT to identify the bug‐tagged issues that are more likely to be fixed by project developers. Our approach incorporates both text and non‐text features to train a neural network‐based prediction model. The experimental results show that our approach achieves an average F1‐score of 0.66 in inter‐project setting, and the F1‐score increase to 0.77 when adding part of testing data for training.