Fault localization based on wide & deep learning model by mining software behavior

Abstract

Many learning based fault localization approaches haven been proposed to improve the effectiveness by fusing various dimension of fault diagnosis features. However, method calls behavior has been neglected, and the interaction between features has not been fully explored. To solve this problem, firstly, a fault localization method by mining software behavior graphs has been proposed to improve the effectiveness of localizing function call related faults. Then, a fault localization approach by wide & deep learning on multi-feature groups has been proposed. Not only the spectrum based and mutation based suspiciousness features have been analyzed, but also the behavior based and invariants based suspiciousness, the static metrics, as well as the combined features of crash stack trace with the invariants change features have been integrated. Wide & Deep model is adopted as the ranking model, to explore the relationships between these features, so as to improve the effectiveness of fault localization. Experiments on practical software defects benchmark Defects4J have shown that our model outperforms the traditional spectrum-based and mutation-based approaches, it also outperforms the state-art-of learning-based approaches on the capability of early fault detection.