Context-Aware Neural Fault Localization
Zhuo Zhang, Yan Lei, Xiaoguang Mao, Meng Yan, Xin Xia, David Lo
Abstract
Numerous fault localization techniques identify suspicious statements potentially responsible for program failures by discovering the statistical correlation between test results (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">failing</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passing</i> ) and the executions of the different statements of a program (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covered</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not covered</i> ). They rarely incorporate a failure context into their suspiciousness evaluation despite the fact that a failure context showing how a failure is produced is useful for analyzing and locating faults. Since a failure context usually contains the transitive relationships among the statements of causing a failure, its relationship complexity becomes one major obstacle for the context incorporation in suspiciousness evaluation of fault localization. To overcome the obstacle, our insight is that leveraging the promising learning ability may be a candidate solution to learn a feasible model for incorporating a failure context into fault localization. Thus, we propose a context-aware neural fault localization approach (CAN). Specifically, CAN represents the failure context by constructing a program dependency graph, which shows how a set of statements interact with each other (i.e., data and control dependencies) to cause a failure. Then, CAN utilizes graph neural networks to analyze and incorporate the context (e.g., the dependencies among the statements) into suspiciousness evaluation. Our empirical results on the 12 large-sized programs show that CAN achieves promising results (e.g., 29.23% faults are ranked within top 5), and it significantly improves the state-of-the-art baselines with a substantial margin.