IJBTT

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, https://doi.org/10.14445/22312803/IJCTT-V71I6P108

Abstract
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.

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

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