Semantic fault diagnosis: automatic natural-language fault descriptions


Before a fault can be fixed, it first must be understood. However, understanding why a system fails is often a difficult and time consuming process. While current automated-debugging techniques provide assistance in knowing where a fault is, developers are left unaided in understanding what a fault is, and why the system is failing. We present Semantic Fault Diagnosis (SFD), a technique that leverages lexicographic and dynamic information to automatically capture natural-language fault descriptors. SFD utilizes class names, method names, variable expressions, developer comments, and keywords from the source code to describe a fault. SFD can be used immediately after observing a failing execution and requires no input from developers or bug reports. In addition we present motivating examples and results from a SFD prototype to serve as a proof of concept.

Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering