Aging-related Bug Prediction based on multi-view Graph Feature Learning and Graph-Transformer

Software aging, characterized by an increasing failure rate or performance degradation in long-running software systems, poses significant risks, including substantial financial losses and potential threats to human lives. This phenomenon is primarily driven by the accumulation of runtime errors, commonly referred to as aging-related bugs (ARBs). Aging-related bug prediction (ARBP) has been proposed to facilitate the detection and remediation of ARBs prior to software release. However, ARBP’s effectiveness heavily depends on the quality of dataset features used. Previous research has largely relied on a standard set of manually designed metrics, often overlooking that these metrics may fail to distinguish between code segments with different semantics, even when they exhibit identical metric values. While some studies have attempted to develop models that learn semantic features from source code, they typically focus on token-level or graph-level features, neglecting a comprehensive exploration of ARB characteristics within the source code. Specifically, there is insufficient discussion on whether deep semantic features can adequately capture the essential traits that trigger aging phenomena. In this paper, we propose a novel multi-view graph feature learning framework based on Graph-Transformer, which integrates newly proposed ARB features extracted from Abstract Syntax Trees with Code Property Graphs for feature learning. Our approach effectively captures hierarchical structures and variable dependencies, facilitating the identification of complex interactions that contribute to ARBs. Additionally, we implement sub-graph sampling and class imbalance strategies to enhance model performance. Experimental results across three datasets demonstrate that our method surpasses state-of-the-art approaches, a code property graph-based feature extraction method (specifically SGT), achieving precision improvements of 8.2% on Linux, 15.4% on MySQL, and 2.5% on NetBSD, thereby establishing a new benchmark for ARB prediction.