GVFL: Variable Level Fault Localization Using Graph Representation Learning

Abstract

Fault localization is a critical phase in software testing and debugging which significantly impacts the efficiency of software fault repair. Existing fault localization research predominantly focuses on the method or statement level, while variable level localization remains less explored. Although variable level techniques offer finer-grained clues closer to root causes, current approaches typically rely on isolated statistical analysis. They fail to capture the complex structural dependencies among program entities, which limits their effectiveness in tracing error propagation. To address this, we propose GVFL, a variable level fault localization method that utilizes graph representation learning to capture complex structural dependencies. It first employs decision trees to select critical variables strongly associated with failures. Then, it constructs a variable relationship graph to capture the dependencies among these variables. Finally, it utilizes graph representation learning to compute suspiciousness scores for fault localization. To evaluate the performance of GVFL, we conduct an empirical study on 835 real faults from the widely used Defects4J v2.0.0 dataset. The results show that GVFL locates a total of 210, 566, and 844 faulty variable positions on Top-1, Top-5, and Top-10 metrics, respectively. It outperforms state-of-the-art FL techniques, including IsoVar, SmartFL, and VarDT. We further analyze the impact of different graph neural network models and variable attributes on GVFL performance. Experimental results indicate that GGAT achieves the best performance for GVFL. Furthermore, all three variable attributes significantly influence the effectiveness of GVFL, with variable value features being the most important.