Automatic Identification of High Impact Bug Report by Test Smells of Textual Similar Bug Reports

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.