Title: Deep learning-powered test case prioritisation in continuous integration: a comparative study and efficiency analysis

Authors: Sheetal Sharma; Swati V. Chande

Addresses: Rajasthan Technical University, Akelgarh, Kota, Rajasthan 324010, India ' ISIM, International School of Informatics and Management, Mahaveer Marg, Sector 12, Mansarovar, Jaipur, Rajasthan 302020, India

Abstract: The empirical study introduces a deep learning-based approach for prioritising test cases in continuous integration (CI) environments, leveraging historical CI data to optimise resource allocation and reduce testing time. The model achieved a remarkable 100% accuracy in prioritisation, outperforming traditional methods. Compared to decision tree, it achieved perfect accuracy with fewer test cases. Against random forest, it had a higher fault detection rate while maintaining efficiency. When compared to neural network, it struck a balance between fault detection and execution time. This research highlights deep learning's potential in transforming CI/CD testing strategies and software development practices.

Keywords: test case prioritisation; continuous integration; CI; deep learning; comparative analysis; efficiency; accuracy; fault detection; software development.

DOI: 10.1504/IJCVR.2025.147491

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.4, pp.488 - 505

Received: 12 Oct 2023
Accepted: 15 Nov 2023
Published online: 18 Jul 2025 *

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