Performance Test Engineering Practice for Scaled Agile Framework Leveraging Machine Learning and Artificial Intelligence Techniques

© 2023 by IJCTT Journal
Volume-71 Issue-6
Year of Publication : 2023
Authors : Suresh Kannan Duraisamy, Bharath Kumar Maganti, Ravi Pulle
DOI :  10.14445/22312803/IJCTT-V71I6P108

How to Cite?

Suresh Kannan Duraisamy, Bharath Kumar Maganti, Ravi Pulle, "Performance Test Engineering Practice for Scaled Agile Framework Leveraging Machine Learning and Artificial Intelligence Techniques," International Journal of Computer Trends and Technology, vol. 71, no. 6, pp. 47-54, 2023. Crossref,

The Information Technology (IT) industry constantly faces intense demand to meet evolving business & Customer needs, Leading to a proliferation of technology transformation initiatives such as the Scaled Agile Framework (SAFe), Cloud, and Microservices architecture. While these advancements promote the overall time to market, performance testing and other support services face a persistent challenge in keeping up with the speed of testing delivery. This research paper delves into the current state of Performance testing practices, explores the challenges of incorporating it into the SAFe software delivery model, and proposes solutions using DevOps automation, machine learning (ML), and artificial intelligence (AI). The research further highlights the importance of ML models in predicting system performance to accelerate test delivery; this has been demonstrated with a training dataset in Amazon's cloud Machine Learning solution-Sagemaker. This research also yielded a design for a next-generation AI-based expert system that can predict the performance of a software system using ML models and Rule-based engines.

AI & ML in Software Testing, DevOps Automation, Performance Testing, Scaled Agile Testing.


[1] Safe and Agile. [Online]. Available:
[2] Rose de Fremery, How Dynatrace Empowers Performance Engineering Teams to Test at Scale, 2021. [Online]. Available:
[3] Raluca Dovleac, “The Rise of DevQualOps and Implications on Software Quality,” International Journal of Computers, vol. 8, pp. 5- 10, 2023.
[Google Scholar] [Publisher Link]
[4] Suresh Kannan Duraisamy, Bryce Bass, and Sai Mukkavilli, “Embedding Performance Testing in Agile Software Model,” International Journal of Software Engineering & Applications, vol. 12, no. 6, pp. 1-11, 2021.
[CrossRef] [Publisher Link]
[5] Pulasthi Perera, Roshali Silva, and Indika Perera, “Improve Software Quality Through Practicing DevOps,” 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions, 2018.
[CrossRef] [Publisher Link]
[6] Sandesh Achar, “Enterprise SaaS Workloads on New-Generation Infrastructure-as-Code (IaC) on Multi-Cloud Platforms,” Global Disclosure of Economics and Business, vol. 10, no. 2, pp. 55-74, 2021.
[CrossRef] [Publisher Link]
[7] Praveen Bagare, and Desyatnikov, Test Data Management in Software Testing Life Cycle-Business Need and Benefits in Functional, Performance, and Automation Testing.
[Google Scholar] [Publisher Link]
[8] Mantas Dvareckas, 5 Test Data Challenges that Every CTO should know about, 2022. [Online]. Available:
[9] Yogesh K. Dwivedi et al., “Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy,” International Journal of Information Management, vol. 57, p. 101994, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] S. Russell, and P. Norvig, “Artificial Intelligence: A Modern Approach. Pearson Education Limited,” Journal of Service Science and Management, 2019.
[11] Yingnong Dang, Qingwei Lin, and Peng Huang, “AIOps: Real-World Challenges and Research Innovations,” 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Gururaj Hm, Power of AI in Performance Engineering, 2022. [Online]. Available:
[13] Samragyi Chamoli, Implementing AI for Improved Performance Testing, 2023. [Online]. Available:
[14] Dhaya Sindhu Battina, “Artificial Intelligence in Software Test Automation: A Systematic Literature Review,” International Journal of Emerging Technologies and Innovative Research, vol. 6, no. 12, pp. 1329-1332, 2019.
[Google Scholar] [Publisher Link]
[15] Perficient Latin America, How Machine Learning Enhances Performance Engineering and Testing, 2019. [Online]. Available:
[16] Anita Maruti Patil-Nikam, Oriental Institute Usage of Machine Learning Algorithms in Software Testing, 2023.
[Publisher Link]
[17] Lakshmisri Surya, “Machine Learning-future of Quality Assurance,” International Journal of Emerging Technologies and Innovative Research, 2019.
[Publisher Link]
[18] Garry Kranz, Amazon SageMaker. [Online]. Available:
[19] I. Vlahavas et al., “ESSE: An Expert System for Software Evaluation,” Knowledge-based systems, vol. 12, no. 4, pp. 183-197, 1999.
[CrossRef] [Publisher Link]
[20] Fusion Middleware Performance and Tuning Guide. [Online]. Available:
[21] Carmen Vitucci, and Draksha Sharma, Harnessing the Power of AI in Performance Engineering. [Online]. Available: