International Journal of Engineering
Trends and Technology

Research Article | Open Access | Download PDF
Volume 74 | Issue 6 | Year 2026 | Article Id. IJETT-V74I6P121 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I6P121

Deep-TCP: An Intelligent Deep Learning Model for Automated Test Case Prioritization Systems using Neural Networks

Sowmyadevi S, Shashi Mehrotra

Received Revised Accepted Published
05 Jan 2026 30 Mar 2026 20 Apr 2026 27 Jun 2026

Citation :

Sowmyadevi S, Shashi Mehrotra, "Deep-TCP: An Intelligent Deep Learning Model for Automated Test Case Prioritization Systems using Neural Networks," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 6, pp. 294-308, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I6P121

Abstract

Test case prioritization is the process of ordering test cases based on their importance to improve regression testing efficiency. Test case prioritization approaches have been demonstrated to improve regression testing processes. But operating the whole regression test suite can be inconvenient and costly, particularly for large systems. To overcome this problem, a novel An Intelligent Deep Learning Model for Automated Test Case Prioritization Systems using Neural Networks (Deep-TCP) has been proposed. Test cases are collected from the source code repository and split into test steps. The test steps are preprocessed using Normalization, Tokenization and stemming to remove noise. After pre-processing, word embedding is computed and the embedded features are clustered using K-means clustering. The novelty of the proposed integration is where the effort is of word embedding, K-means clustering, Radial Basis Function Network (RBFN), and CNN-BiGRU to capture both semantic and sequential connections between test cases. Radial Basis Function Network (RBFN) is used for extracting the relevant features and Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) is used for testcase priority such as high priority, average priority and low priority. The proposed framework has been implemented using Python (PyTorch) and evaluated on a system with NVIDIA RTX 4090 GPU. The efficacy of the proposed Deep-TCP framework has been determined using evaluation metrics such as Average Percentage of Faults Detected (APFD). Average APFD of the proposed method is 94.5% which is higher than 71.25% at ATRL-TCP, 73.5% at QAOA-TCS and 81.5% at BootQA approaches.

Keywords

Convolutional Neural Network-Bidirectional Gated Recurrent Unit, Fault Detection, K-means clustering, Software Testing, Testcase Prioritization.

References

[1] Syed Roohullah Jan et al., “An Innovative Approach to Investigate Various Software Testing Techniques and Strategies,” International Journal of Scientific Research in Science, Engineering and Technology, vol. 2, no. 2, pp. 682-689, 2016.
[Google Scholar]

[2] Abhijit A. Sawant, Pranit H. Bari, and P.M. Chawan, “Software Testing Techniques and Strategies,” International Journal of Engineering Research and Applications, vol. 2, no. 3, pp. 980-986, 2012.
[Google Scholar]

[3] Mohd. Ehmer Khan, “Different forms of Software Testing Techniques for Finding Errors,” International Journal of Computer Science Issues, vol. 7, no. 3, pp. 11-16, 2010.
[Google Scholar]

[4] Albin Lönnfält et al., “An Intelligent Test Management System for Optimizing Decision Making During Software Testing,” Journal of Systems and Software, vol. 219, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Muhammad Faisal Abrar et al., “A Data-Driven Analysis of Software Testing Automation Challenges using Structural Equation Modeling (SEM) Approach,” IEEE Access, vol. 13, pp. 96159-96181, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Mark Harman, Peter O'Hearn, and Shubho Sengupta, “Harden and Catch for Just-in-Time Assured LLM-based Software Testing: Open Research Challenges,” Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering, Association for Computing Machinery, New York, NY, United States, pp. 1-17, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Zheng Li, Mark Harman, and Robert M. Hierons, “Search Algorithms for Regression Test Case Prioritization,” IEEE Transactions on Software Engineering, vol. 33, no. 4, pp. 225-237, 2007.
[CrossRef] [Google Scholar] [Publisher Link]

[8] N. Gokilavani, and B. Bharathi, “Test Case Prioritization to Examine Software for Fault Detection using PCA Extraction and K-Means Clustering with Ranking,” Soft Computing, vol. 25, no. 7, pp. 5163-5172, 2021.  

[9] Ahmadreza Saboor Yaraghi et al., “Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts,” IEEE Transactions on Software Engineering, vol. 49, no. 4, pp. 1615-1639, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Mohammed Assiri, “Test Case Prioritization using Dragon Boat Optimization for Software Quality Testing,” Electronics, vol. 14, no. 8, pp. 1-20, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Tapas Kumar Choudhury et al., “AnoLSTM: A Deep Learning Approach for Test Cases Prioritization,” Procedia Computer Science, vol. 258, pp. 1793-1803, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Aishwaryarani Behera, and Arup Abhinna Acharya, “An Effective GRU-based Deep Learning Method for Test Case Prioritization in Continuous Integration Testing,” Procedia Computer Science, vol. 258, pp. 4070-4083, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[13]  Jeongki Son et al., “Evaluating Machine Learning-based Test Case Prioritization in the Real World: An Experiment with SAP HANA,” 2025 IEEE Conference on Software Testing, Verification and Validation (ICST), Napoli, Italy, pp. 522-532, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Andreea Vescan, and Cristina-Maria Tiutin, “Test Case Prioritization based on Neural Networks Classification: A Replication Study and Hyper-Parameter Optimization using Taguchi Methods,” IEEE Access, vol. 13, pp. 118082-118095, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Abdallh Mostafa Mohamed Mohamed Menshawy, and Mohammad Nasar, “Optimizing Software Quality: Integrating Test Case Prioritization, Defect Prediction, and Resource Allocation Strategies,” East Journal of Computer Science, vol. 1, no. 2, pp. 16-21, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Hemant Kumar, and Vipin Saxena, “Optimization and Prioritization of Test Cases through the Hungarian Algorithm,” Journal of Advances in Mathematics and Computer Science, vol. 40, no. 3, pp. 61-72, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Abdulkarim Bello, and Hauwa Muhammed Alhassan, “Cost-Cognizant Test Case Prioritization for Software Product Line using Genetic Algorithm,” FUDMA Journal of Sciences, vol. 9, no. 9, pp. 129-138, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Shounak Rushikesh Sugave et al., “Fault-Aware Test Case Prioritization in Software Testing using Jaya Archimedes Optimization Algorithm,” Journal of Electronic Testing, vol. 41, no. 1, pp. 41-61, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Qingran Su et al., “Attention Transfer Reinforcement Learning for Test Case Prioritization in Continuous Integration,” Applied Sciences, vol. 15, no. 4, pp. 1-22, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Antonio Trovato, Martin Beseda, and Dario Di Nucci, “A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection,” Proceedings of the 2025 29th International Conference on Evaluation and Assessment in Software Engineering Companion, Association for Computing Machinery, New York, NY, United States, pp. 56-60, 2025. [CrossRef] [Google Scholar] [Publisher Link]

[21] Christoph Laaber, Tao Yue, and Shaukat Ali, “Evaluating Search-based Software Microbenchmark Prioritization,” IEEE Transactions on Software Engineering, vol. 50, no. 7, pp. 1687-1703, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[22] Xinyi Wang et al., “Test Case Minimization with Quantum Annealers,” ACM Transactions on Software Engineering and Methodology, vol. 34, no. 1, pp. 1-24, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Zikang Zhang et al., “Exploiting DBSCAN and Combination Strategy to Prioritize the Test Suite in Regression Testing,” IET Software, vol. 2024, no. 1, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[24] Aizaz Sharif, Dusica Marijan, and Marius Liaaen, “DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing,” 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME), Luxembourg, pp. 525-534, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Ada John, Isreal John, and Trump Dion, “Integrating AI-Driven Test Case Optimization into Continuous Integration/Continuous Delivery (CI/CD) Pipeline Authors,” Continuous Delivery (CI/CD) Pipeline Authors, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Zheng Li et al., “An Environment Adaptation Agent of Reinforcement Learning in Continuous Integration Test Case Prioritization,” International Journal of Software Engineering and Knowledge Engineering, vol. 36, no. 2, pp. 311-341, 2026.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Ajmer Singh et al., “A Systematic Literature Review on Test Case Prioritization Techniques,” Agile Software Development, pp. 101-159, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[28] Allan Mori, Ana C.R. Paiva, and Simone R.S. Souza, “Code Change and Smell Techniques for Regression Test Selection,” Software Quality Journal, vol. 33, no. 1, pp. 1-24, 2025.
[CrossRef] [Google Scholar] [Publisher Link]