A Feature-Driven Framework for Software Fault Prediction

TL;DR

This paper presents a feature-driven framework combining feature selection and parameter tuning to enhance machine learning-based software fault prediction, achieving high accuracy and robustness.

Contribution

It introduces a combined approach of feature selection methods and hyperparameter tuning techniques to improve fault prediction performance.

Findings

CFS and GA combination achieved 88.40% accuracy with RF.

Feature selection reduced dimensionality and identified key attributes.

Proposed methods showed robustness with minimal variability and improved efficiency.

Abstract

Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software quality and reduce maintenance costs. This research investigates the combined effects of feature selection and parameter tuning on the performance of machine learning (ML) models for SFP. This study evaluates the interaction between feature selection methods, including correlation-based feature selection (CFS), recursive feature elimination (RFE), mutual information (MI), and L1 regularization, where hyperparameter tuning techniques such as grid search, randomized search, and genetic algorithm (GA) are used for optimization of ML algorithms, including random forest (RF), logistic regression (LR), and support vector machines (SVM) for optimized fault prediction performance. The combined application of CFS and GA yielded the highest accuracy, achieving 88.40% with RF, representing an improvement of 18% over baseline models without feature selection or tuning. Feature selection reduced dimensionality and identified critical attributes such as weighted methods per Class (WMC) and coupling between objects (CBO), while iterative parameter tuning optimized model alignment to these feature sets. Notably, the proposed methods demonstrated robustness, with minimal cross-validation variability (+-1.0%), and efficiency, reducing training times in univariate methods such as L1 regularization.