AI-Enhanced Test Prioritization in Continuous Integration for SaaS Platforms
Keywords:
AI-driven testing, continuous integration, SaaS platforms, deep learningAbstract
Continuous integration pipelines using AI driven test prioritization for Software-as-a-Service (SaaS) platforms is crucial advancement in software quality assurance which leverages deep learning techniques to optimise regression testing. The aim of this research is to investigate the integration of convolutional neural network and anomaly detection algorithm to prioritise test cases which is based on historical execution of data and real-time cloud-based telemetry, including Grafana dashboards and Google Cloud logs.
Downloads
References
G. Luo, J. Wu, and L. Ma, "AI-Based Test Case Prioritization for Continuous Integration," IEEE Transactions on Software Engineering, vol. 48, no. 5, pp. 1432–1448, May 2022.
R. Just, D. Jalali, and M. D. Ernst, "Defects4J: A Database of Existing Faults to Enable Controlled Testing Studies for Java Programs," in Proceedings of the 2014 International Symposium on Software Testing and Analysis (ISSTA), San Jose, CA, USA, 2014, pp. 437–440.
M. Z. Iqbal, M. U. Bhatti, and A. Arcuri, "Machine Learning-Based Test Prioritization Techniques for CI Pipelines," IEEE Access, vol. 9, pp. 112345–112361, Aug. 2021.
T. Chen, X. Xia, D. Lo, and J. Grundy, "Machine Learning-Based Test Case Prioritization: A Systematic Literature Review," ACM Computing Surveys (CSUR), vol. 54, no. 5, pp. 1–36, Jun. 2022.
S. Elbaum, G. Rothermel, and J. Penix, "Techniques for Improving Regression Testing in Continuous Integration," in Proceedings of the 2014 ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), San Diego, CA, USA, 2014, pp. 234–245.
Y. Kim, H. Kim, and D. Bae, "Deep Learning-Based Regression Test Selection for Large-Scale Software Systems," Journal of Systems and Software, vol. 179, pp. 110849, Nov. 2021.
C. Zhang, J. Li, X. Chen, and H. Lu, "Anomaly Detection in CI/CD Pipelines Using Recurrent Neural Networks," IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 1–12, Jan. 2023.
A. Arcuri and L. Briand, "A Practical Guide for Using Statistical Tests to Assess Randomized Algorithms in Software Engineering," in Proceedings of the 2011 ACM SIGSOFT Symposium on Foundations of Software Engineering (FSE), Szeged, Hungary, 2011, pp. 1–14.
K. Goseva-Popstojanova and D. S. Wilkins, "Deep Learning for Automated Fault Prediction in Continuous Deployment," IEEE Software, vol. 38, no. 4, pp. 63–70, Jul. 2021.
J. Nam, S. Kim, and H. White, "Automated Test Case Prioritization Using Multi-Objective Optimization," Empirical Software Engineering, vol. 26, no. 2, pp. 23–41, Feb. 2022.
Y. Li, Z. He, and C. Fan, "Test Case Prioritization Using Reinforcement Learning," in Proceedings of the 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME), Luxembourg, 2021, pp. 189–200.
X. Xia, D. Lo, and S. Wang, "Learning to Prioritize Test Cases via Attention-Based Deep Neural Networks," in Proceedings of the 2022 IEEE/ACM International Conference on Automated Software Engineering (ASE), Melbourne, Australia, 2022, pp. 201–212.
G. Rothermel, R. H. Untch, and C. Chu, "Prioritizing Test Cases for Regression Testing," IEEE Transactions on Software Engineering, vol. 27, no. 10, pp. 929–948, Oct. 2001.
J. Zhang, X. Zhu, and Y. Zhuang, "A Hybrid AI Framework for Intelligent Test Selection in CI Pipelines," ACM Transactions on Software Engineering and Methodology (TOSEM), vol. 31, no. 6, pp. 1–25, Dec. 2022.
A. Bertolino, E. Marchetti, and R. Mirandola, "AI-Driven Test Prioritization for Evolving Software Systems," in Proceedings of the 2020 IEEE International Conference on Software Testing, Verification and Validation (ICST), Porto, Portugal, 2020, pp. 210–221.
L. Ma, F. Su, and T. Wang, "An Empirical Study on the Effectiveness of AI-Based Test Case Prioritization," IEEE Transactions on Software Engineering, vol. 50, no. 2, pp. 231–245, Feb. 2024.
S. Yoo and M. Harman, "Regression Testing Minimization, Selection and Prioritization: A Survey," Software Testing, Verification & Reliability, vol. 22, no. 2, pp. 67–120, Mar. 2012.
J. Ledru, P. Mounier, and S. Sapin, "Machine Learning for CI/CD Pipeline Test Optimization," in Proceedings of the 2021 ACM SIGSOFT Symposium on Software Testing and Analysis (ISSTA), Beijing, China, 2021, pp. 456–469.
P. Peterson and R. Kumar, "AI-Driven Automated Test Prioritization in Continuous Deployment," IEEE Software, vol. 37, no. 6, pp. 91–98, Nov. 2020.
R. L. Nord, J. Bosch, and E. Engström, "Software Architecture and Continuous Integration: AI-Based Testing Strategies," IEEE Software, vol. 40, no. 2, pp. 31–39, Mar. 2023.
