Author : Abhishek Goyal

JIRA automation combined with Artificial Intelligence (AI) has revolutionised defect management and agile processes in software development. This paper aims to review the role that AI methods, including ML and NLP, play in improving the ability to detect defects, minimise the use of test cases, and automate testing. With such tools as customisable as well as agiler and reportable workflows and integrated as well as AI analytic representations, JIRA helps development teams anticipate defects during the working process, arrange priorities and productivity. These benefits are real and cases are showing that defect rates drastically diminished, defect fixes are done so much quicker, and decision-making is improved with this synergy. Possible future developments are the improvement of AI models, integration of new technologies into those models, the implementation of the solutions for widespread teams, and the problems of ethical implementations in AI-driven automation. The paper has presented the current and potential scenarios of AI augmentation to JIRA for improving software testing and defect tracking.

Keywords: AI-powered testing, JIRA automation, defect management, agile workflows, machine learning (ML), natural language processing (NLP), test case optimisation, software development, predictive defect detection