Automatic prediction of the severity of bugs using stack traces and categorical features

摘要

Context: The severity of a bug is often used as an indicator of how a bug negatively affects system functionality. It is used by developers to prioritize bugs which need to be fixed. The problem is that, for various reasons, bug submitters often enter the incorrect severity level, delaying the bug resolution process. Techniques that can automatically predict the severity of a bug can significantly reduce the bug triaging overhead. In our previous work, we showed that the accuracy of description-based severity prediction techniques could be significantly improved by using stack traces as a source of information.Objective: In this study, we expand our previous work by exploring the effect of using categorical features, in addition to stack traces, to predict the severity of bugs. These categorical features include faulty product, faulty component, and operating system. We experimented with other features and observed that they do not improve the severity prediction accuracy. A Software system is composed of many products; each has a set of components. Components interact with each to provide the functionality of the product. The operating system field refers to the operating system on which the software was running on during the crash.Method: The proposed approach uses a linear combination of stack trace and categorical features similarity to predict the severity. We adopted a cost sensitive K Nearest Neighbor approach to overcome the unbalance label distribution problem and improve the classifier accuracy.Results: Our experiments on bug reports of Eclipse submitted between 2001 and 2015 and Gnome submitted between 1999 and 2015 show that the accuracy of our severity prediction approach can be improved from 5% to 20% by considering categorical features, in addition to stack traces.Conclusion: The accuracy of predicting the severity of bugs is higher when combining stack traces and three categorical features, product, component, and operating system.