Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

TL;DR

This paper introduces ConDF, a novel framework combining feature selection and a simplified deep forest model to accurately predict crash fault residence, significantly improving over existing methods in software fault localization.

Contribution

The paper presents a new crash fault residence prediction framework that integrates feature subset selection with a simplified deep forest model, enhancing prediction accuracy.

Findings

ConDF outperforms 17 baseline methods on three performance metrics.

Feature selection reduces dimensionality and improves model performance.

Experiments on seven open source projects validate the effectiveness of ConDF.

Abstract

The software inevitably encounters the crash, which will take developers a large amount of effort to find the fault causing the crash (short for crashing fault). Developing automatic methods to identify the residence of the crashing fault is a crucial activity for software quality assurance. Researchers have proposed methods to predict whether the crashing fault resides in the stack trace based on the features collected from the stack trace and faulty code, aiming at saving the debugging effort for developers. However, previous work usually neglected the feature preprocessing operation towards the crash data and only used traditional classification models. In this paper, we propose a novel crashing fault residence prediction framework, called ConDF, which consists of a consistency based feature subset selection method and a state-of-the-art deep forest model. More specifically, first, the feature selection method is used to obtain an optimal feature subset and reduce the feature dimension by reserving the representative features. Then, a simplified deep forest model is employed to build the classification model on the reduced feature set. The experiments on seven open source software projects show that our ConDF method performs significantly better than 17 baseline methods on three performance indicators.