Driving Continuous Improvement in Engineering Projects with AI-Enhanced Agile Testing and Machine Learning
Abhishek Goyal · International Journal of Advanced Research in Science Communication and Technology · 2023
Continuous improvement projects follow unique selection, deployment, and tracking processes. In today’s fast-paced engineering projects, it has become crucial to drive actionable change for continuous improvement to support the delivery of success. Software development has been a major adopter of agile approaches because of the iterative nature of the process and how well it fits the ever-changing goals of engineering projects. This article delves into the topic of how software development and engineering project management may be improved by using AI and ML in Agile testing procedures. With the help of AI, working in teams, it is possible to sort out routine processes, create more test cases, and use obtained outcomes to reasonably distribute resources and time frames. Data mining, for example, helps in predictive analysis in order to detect risks and any possible slowdown for further action to be taken. The ability to receive constant feedback from AI reinforces the Agile testing process to thereby enable fast identification of problems, better organisational cooperation and successful implementation of intricate projects. This paper discusses the methodologies of Agile testing, a role of AI in enhancing testing efficiency, and best practices for incorporating AI in Agile engineering projects