Survey of Predict DevOps Readiness based on Machine Learning

Abstract

In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has given software more reliability, and enable to scalable within a little period. Software development is became is more effective in world today in become a core competency for every business also the machine learning is as obtained   interest. As companies increasingly use DevOps practices to enhance efficiency, quality, and timely software delivery, predicting DevOps readiness becomes increasingly vital and important to ensure successful software implementation. the contribution of this paper  presents a critical framework for machine learning and its use in predicting DevOps readiness in organizations through team collaboration and workflow monitoring using machine learning models and algorithms that are used for optimization, accuracy, and effective delivery of good results.

Country : Iraq

1 Zahraa Nuri Hasan2 Ibrahim Ahmed Saleh

  1. Student, Department of Software, College of Computer & Math., University of Mosul, Iraq
  2. Professor, Department of Software, College of Computer & Math., University of Mosul, Iraq

IRJIET, Volume 9, Issue 5, May 2025 pp. 98-103

doi.org/10.47001/IRJIET/2025.905013

References

  1. Altaie, A. M., Hamo, A. Y., & Alsarraj, R. G. (2021). Software Fault Estimation Tool Based on Object-Oriented Metrics. Iraqi Journal of Science, 63-69.
  2. Qassem, D. Y., & Al_saati, N. A. (2023). A Solution to the Next Release Problem by Swarm Intelligence.
  3. A. Fadhil, Anfal & Albayati, Asmaa & Saleh, Ibrahim. (2025). Develop Approach to Predicate Software Reliability Growth Models Parameters Based on Machine learning. 10.25195/ijci.v49i2.509.
  4. Abdulmajeed, A. A., Al-Jawaherry, M. A., & Tawfeeq, T. M. (2021, May). Predict the required cost to develop Software Engineering projects by Using Machine Learning. In Journal of Physics: Conference Series (Vol. 1897, No. 1, p. 012029). IOP Publishing.
  5. Majeed, N., & Ramo, F. (2022). Performance Evaluation of the Ensemble and Selected Machine Learning Techniques. JMCER, 2022, 94-100.
  6. Sriraman, G. (2023). A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment. Frontiers in Computer Science, 5, 1214722.
  7. Karamitsos, I., Albarhami, S., & Apostolopoulos, C. (2020). Applying DevOps practices of continuous automation for machine learning. Information, 11(7), 363.
  8. Mahida, A. A Review on Continuous Integration and Continuous Deployment (CI/CD) for Machine Learning.
  9. Battina, D. S. (2019). An intelligent devops platform research and design based on machine learning. Training, 6(3).
  10. Noorani, N. M., Zamani, A. T., Alenezi, M., Shameem, M., & Singh, P. (2022). Factor prioritization for effectively implementing DevOps in software development organizations: a SWOT-AHP approach. Axioms, 11(10), 498.
  11. Krishna, M. Y. S., & Gawre, S. K. (2023). MLOps for Enhancing the Accuracy of Machine Learning Models using DevOps, Continuous Integration, and Continuous Deployment. Research Reports on Computer Science, 97-103.
  12. Jha, A. V., Teri, R., Verma, S., Tarafder, S., Bhowmik, W., Kumar Mishra, S.,... & Philibert, N. (2023). From theory to practice: Understanding DevOps culture and mindset. Cogent Engineering, 10(1), 2251758.
  13. Yarlagadda, R. T. (2018). Understanding DevOps & bridging the gap from continuous integration to continuous delivery. Understanding DevOps &Bridging the Gap from Continuous Integration to Continuous Delivery', International Journal of Emerging Technologies and Innovative Research (www. jetir. org), ISSN, 2349-5162.
  14. Tajammul, M. (2022). DevOps CI Automation.
  15. Salih, A. M., Syed-Mohamad, S. M., Keikhosrokiani, P., & Samsudin, N. H. (2023). Adopting DevOps practices: an enhanced unified theory of acceptance and use of technology framework. International Journal of Electrical & Computer Engineering (2088-8708), 13(6).
  16. Swamy, H. (2022). Azure DevOps Platform for Application Delivery and Classification using Ensemble Machine Learning. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 19(1).
  17. Swamy, H. (2024). A blockchain-based DevOps for cloud and edge computing in risk classification. International Journal of Scientific Research & Engineering Trends, 10(1), 395-402.
  18. Dhaliwal, N. (2020). Validating software upgrades with ai: ensuring devops, data integrity and accuracy using CI/CD pipelines. Journal of Basic Science and Engineering, 17(1).
  19. Mallreddy, S. R., & Vasa, Y. (2023). Predictive Maintenance In Cloud Computing And Devops: Ml Models For Anticipating And Preventing System Failures. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO, 10(1), 213-219.
  20. Fawzy, A. H., Wassif, K., & Moussa, H. (2023). Framework for automatic detection of    anomalies in DevOps. Journal of King Saud University-Computer and Information Sciences, 35(3), 8-19.
  21. Chowdary, M. N., Sankeerth, B., Chowdary, C. K., & Gupta, M. (2022, August). Accelerating the Machine Learning Model Deployment using MLOps. In Journal of Physics: Conference Series (Vol. 2327, No. 1, p. 012027). IOP Publishing.
  22. Shah, P. S. U., Ahmad, N., & Beg, M. O. (2024). Towards MLOps: A DevOps Tools Recommender System for Machine Learning System. arXiv preprint arXiv:2402.12867.