Comparative Analysis of Ensemble classifiers over Machine Learning Classifiers for Early Software Quality Prediction

Anuradha Sharma; Kumar Amrendra; Piyush Ranjan

doi:10.2991/978-94-6463-787-8_29

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Comparative Analysis of Ensemble classifiers over Machine Learning Classifiers for Early Software Quality Prediction

Authors

Anuradha Sharma¹^{, 2}^{, *}, Kumar Amrendra², Piyush Ranjan²

¹Research Scholar, Jharkhand Rai University, Ranchi, India

²Department of CSE & IT, Jharkhand Rai University, Ranchi, India

^*Corresponding author. Email: anuradha.shrama85@gmail.com

Corresponding Author

Anuradha Sharma

Available Online 17 July 2025.

DOI: 10.2991/978-94-6463-787-8_29 How to use a DOI?
Keywords: Software Quality; Decision table; J48; Random tree; XGBoost; Gradient Boosting Machine; AdaBoost
Abstract: Regardless of the type of software system under development, ensuring the delivery of high-quality software within the constraints of time and budget is essential for many software enterprises. The choice of a software process model significantly influences the overall system quality, as fault that go undetected for longer periods become increasingly challenging to address. Early prediction of software quality can greatly support developers in activities related to software maintenance and quality assurance while enabling more effective allocation of effort and resources. This paper presents a comparative analysis conducted among Decision table, J48, Naïve Bayes, Random tree, XGBoost, AdaBoost, gradient boosting machine. The evaluation focuses on four commonly utilized performance metrics: Accuracy, Precision, F1 score and Recall.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
Series: Advances in Intelligent Systems Research
Publication Date: 17 July 2025
ISBN: 978-94-6463-787-8
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-787-8_29 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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ris enw bib

TY  - CONF
AU  - Anuradha Sharma
AU  - Kumar Amrendra
AU  - Piyush Ranjan
PY  - 2025
DA  - 2025/07/17
TI  - Comparative Analysis of Ensemble classifiers over Machine Learning Classifiers for Early Software Quality Prediction
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 351
EP  - 366
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_29
DO  - 10.2991/978-94-6463-787-8_29
ID  - Sharma2025
ER  -

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