Cut to the Chase: An Error-Oriented Approach to Detect Error-Handling Bugs

Abstract

Error-handling bugs are prevalent in software systems and can result in severe consequences. Existing works on error-handling bug detection can be categorized into template-based and learning-based approaches. The former requires much human effort and is difficult to accommodate the software evolution. The latter usually focuses on errors of API and assumes that error handling should be right after the handled error. Such an assumption, however, may affect both learning and detecting phases. The existing learning-based approaches can be regarded as API-oriented, which starts from an API and learns if the API requires error handling. In this paper, we propose EH-Digger, an ERROR-oriented approach, which starts from an error handling. Our approach can learn why the error occurs and when the error has to be handled. We conduct a comprehensive study on 2,322 error-handling code snippets from 22 widely used software systems across 8 software domains to reveal the limitation of existing approaches and guide the design of EH-Digger. We evaluated EH-Digger on the Linux Kernel and 11 open-source applications. It detected 53 new bugs confirmed by the developers and 71 historical bugs fixed in the latest versions. We also compared EH-Digger with three state-of-the-art approaches, 30.1% of bugs detected by EH-Digger cannot be detected by the existing approaches.