Transforming QA Efficiency: Leveraging Predictive Analytics to Minimize Costs in Business-Critical Software Testing for the US Market - Joy, Md Shaikat Alam; Alam, Gazi Touhidul; Bakhsh, Mohammed Majid (Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024)
In the context of information assurance, specifically software testing, predictive analytics has rapidly become the ‘go-to’ solution for application QA. In this article, the author discusses the adaptation of this technology in the QA processes and its aim to optimize the processes, decrease costs and increase the quality of the software product in the USA. The study shows that through analysis of data, testing cycles can be managed effectively and defects detected before the time and resource is spent on developing and testing the unnecessary features. Main milestones are described in the paper, including data gathering, machine learning algorithms, and feedback, which show how they shifted traditional approaches to QA. Moreover, it goes a step further and discusses the application of the solution such as cost saving, efficiency and ways of decision making. This article also looks at the difficulties organizations encounter while implementing these tools such as technical issues as well as resistance from the organization and ways which can be used to ensure a proper implementation of the predictive analytics. Finally, the paper defines tendencies for the nearest future like future uses of AI in QA processes and interaction with DevOps, accentuating on their capability to contribute in the continuous advancement of software testing. The article provides practical examples of using predictive analytics in QA and demonstrates how companies can obtain tangible enhancements in product quality and reduce expenses. Therefore, the work’s conclusions could be summarized as a call to adapt and adopt predictive analytics due to the current fast pace of market evolution in software.