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Enhancing Software Defect Prediction with Ensemble Models based on Defect Relations Rule Learning

by Sareddy Shiva Reddy, Suresh Pabbojur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 42
Year of Publication: 2024
Authors: Sareddy Shiva Reddy, Suresh Pabbojur
10.5120/ijca2024924029

Sareddy Shiva Reddy, Suresh Pabbojur . Enhancing Software Defect Prediction with Ensemble Models based on Defect Relations Rule Learning. International Journal of Computer Applications. 186, 42 ( Sep 2024), 7-14. DOI=10.5120/ijca2024924029

@article{ 10.5120/ijca2024924029,
author = { Sareddy Shiva Reddy, Suresh Pabbojur },
title = { Enhancing Software Defect Prediction with Ensemble Models based on Defect Relations Rule Learning },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2024 },
volume = { 186 },
number = { 42 },
month = { Sep },
year = { 2024 },
issn = { 0975-8887 },
pages = { 7-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number42/enhancing-software-defect-prediction-with-ensemble-models-based-on-defect-relations-rule-learning/ },
doi = { 10.5120/ijca2024924029 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-09-30T23:02:41+05:30
%A Sareddy Shiva Reddy
%A Suresh Pabbojur
%T Enhancing Software Defect Prediction with Ensemble Models based on Defect Relations Rule Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 42
%P 7-14
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software defect prediction (SDP) is a crucial aspect of software quality assurance, aiming to identify potential defects early in the development process to enhance reliability and reduce maintenance costs. This paper presents a defect relations rule learning (DRRL) to enhance the defect classification models. It discovers the rules based on the defect relation association and applies a rule-ranking mechanism to perform a two-stage prediction model for accurate defect prediction software modules. In the first stage Random Forest, Support Vector Machine, and Naïve Bayes — are employed to analyze their prediction accuracy. In the second stage, an Ensemble Voting Model (EVM) with classifiers prediction outcome for enhancing the accuracy and reliability of defect detection is proposed. The EVM was implemented and evaluated further to validate previous models for effectiveness. The EVM with the proposed DRRL exhibited superior performance of 99.2% accuracy for the CM11 dataset, 88.2% accuracy for the JM1 dataset, and 100% accuracy for the PC1 and PC4 datasets. These findings underscore the model's potential to significantly improve software defect prediction.

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Index Terms

Computer Science
Information Sciences
Software Engineering
Defect Prediction
Machine Learning
Classification

Keywords

Software Defect Prediction Defect Relations Rule Machine Learning Ensemble Model

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