Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Technology Informatics and Engineering
John Ndia
Information Technology. School of Computing and Information Technology. Murang'a University of Technology, Murang’a, Kenya. 75-10200
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
A Systematic Literature Review on Software Testing Prediction Models Job Onyinkwa Osoro; John Ndia
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.526

Abstract

Software testing prediction models play a critical role in improving software quality, reducing faults, and optimizing testing. Although past studies show that these models have improved over time, little focus has been given to them. Many studies rely on limited datasets that do not fully capture the complexity of real software systems, which limits how well the models can generalize. There is also insufficient evidence on how these models use existing datasets in practical, real-life settings, since most evaluations are conducted under controlled or experimental conditions. In addition, key aspects such as interpretability, generalizability, and practical usability are still not adequately addressed, which reduces trust and slows down adoption in practice. This study presents a systematic literature review of recent empirical research on predictive approaches in software testing, focusing on machine learning, deep learning, and hybrid techniques. A structured methodology was used, including clearly defined inclusion and exclusion criteria, systematic searches across major academic databases, quality assessment, and data extraction from 22 selected studies. The analysis considered model types, datasets, feature methods, evaluation metrics, and methodological approaches. The findings show a shift from traditional statistical models to advanced artificial intelligence techniques, including graph neural networks, contrastive learning, and deep fuzzy clustering. In addition, optimization and data balancing techniques improve predictive performance, while explainable artificial intelligence enhances model interpretability. However, challenges such as limited cross-project generalization and insufficient industrial validation still exist. 
Co-Authors Job Onyinkwa Osoro