A hybrid chaos-based feature selection and machine learning for improved software defect prediction

Precision Software Defect Prediction Using Novel Machine Learning Approaches

This study aims to improve software defect prediction five publicly available NASA datasets by using Random Forest and Classification Network to achieve higher defect prediction accuracy compared to methods without feature selection (WOFS) and to get matrix problem the authors use Classification Network.

Cross Project Software Defect Prediction Using Machine Learning with Optimized Feature Selection

In smart city software systems, where interconnected services demand high reliability, Software Defect Prediction (SDP) plays a vital role and reducing maintenance costs by identifying defect-prone modules early in the Software Development Life Cycle (SDLC). Cross-Project Defect Prediction (CPDP) enables defect data fr...

Research on Software Defect Prediction Based on Static and Dynamic Feature Fusion and Machine Learning

Aiming at the problems that the static-dynamic feature fusion mechanism lacks systematic multi-scenario verification, the feature-model adaptation law is unclear, and the engineering practicability of existing research conclusions is insufficient, this paper proposes a static-dynamic feature fusion defect prediction me...

Automated fault detection and predictive quality control in software system using advanced deep learning techniques

A Hybrid Feature Selection and Ensemble Learning Framework for Software Defect Prediction with Class Imbalance Handling

Software defect prediction (SDP) is essential for improving software quality since it finds error-prone modules early in the development lifecycle. Current methods produce inflated and erroneous performance metrics because of data leaks, inadequate class imbalance management, and reliance on antiquated classifiers. By...

Comparative Analysis of Ensemble Learning Methods for Software Reliability Prediction

The results demonstrate that ensemble methods provide superior performance in identifying reliability levels, and which classification method is most suitable for predicting software reliability based on code metrics such as Cyclomatic Complexity and Halstead Volume.