Bug reports are submitted by the software stakeholders to foster the location and elimination of bugs. However, in large-scale software systems, it may be impossible to track and solve every bug, and thus developers should pay more attention to High-Impact Bugs (HIBs). Previous studies analyzed textual descriptions to automatically identify HIBs, but they ignored the quality of code, which may also indicate the cause of HIBs. To address this issue, we integrate the features reflecting the quality of production (i.e. CK metrics) and test code (i.e. test smells) into our textual similarity based model to identify HIBs. Our model outperforms the compared baseline by up to 39% in terms of AUC-ROC and 64% in terms of F-Measure. Then, we explain the behavior of our model by using SHAP to calculate the importance of each feature, and we apply case studies to empirically demonstrate the relationship between the most important features and HIB. The results show that several test smells (e.g. Assertion Roulette, Conditional Test Logic, Duplicate Assert, Sleepy Test) and product metrics (e.g. NOC, LCC, PF, and ProF) have important contributions to HIB identification.

Bug reports are written by the software stakeholders to track software defects and vulnerabilities. Since Software Quality Assurance (SQA) resources are limited, developers tend to resolve High-Impact Bugs (HIB) in advance. Prior research identified HIBs by analyzing the textual information in bug reports. However, they only consider textual information instead of the root cause of bugs, such as code quality. Since prior study revealed software test smells (i.e., sub-optimal test code implementation) are related to bug proneness, we intend to measure test smell distribution in textual similar bug reports to identify HIB reports. We first construct an effective model, which outperforms the baseline by 29.3% in terms of AUC-ROC. Secondly, we use SHAP to compute the importance of test smell features. Finally, we conduct an empirical survey to discuss the relationship between test smell and HIB reports. Result shows that Assertion Roulette and Conditional Test Logic test smell are important factors in distinguishing the types of bug reports.