Identifying bug signatures using discriminative graph mining

Bug localization has attracted a lot of attention recently. Most existing methods focus on pinpointing a single statement or function call which is very likely to contain bugs. Although such methods could be very accurate, it is usually very hard for developers to understand the context of the bug, given each bug location in isolation. In this study, we propose to model software executions with graphs at two levels of granularity: methods and basic blocks. An individual node represents a method or basic block and an edge represents a method call, method return or transition (at the method or basic block granularity). Given a set of graphs of correct and faulty executions, we propose to extract the most discriminative subgraphs which contrast the program flow of correct and faulty executions. The extracted subgraphs not only pinpoint the bug, but also provide an informative context for understanding and fixing the bug. Different from traditional graph mining which mines a very large...

• Type: Conference
• Year: 2009
• Where: ISSTA
• Authors: Hong Cheng, David Lo, Yang Zhou, Xiaoyin Wang, Xifeng Yan
• Keywords: Basic Block, Bug Localization, Discriminative Subgraphs, ISSTA 2009, Software Engineering