Artificial intelligence for enhanced quality assurance through advanced strategies and implementation in the software industry

Abstract In this study, an artificial intelligence (AI) assistant is developed specifically for quality assurance (QA) tasks in response to the software industry’s growing demand for better QA solutions. Traditional QA methods are labor-intensive and prone to human error, even when they function properly. Utilizing the most recent advancements in natural language processing and AI, this AI assistant maximizes output and reliability by automating and optimizing QA processes. Using Rasa technology, the AI assistant aims to revolutionize QA by offering testers comprehensive support, including question-answering, contextual recommendations, and expanding users’ knowledge bases. It can reply to queries using both words and images. They are minimizing the amount of time spent searching for answers and raising user engagement. Notwithstanding AI’s potential to improve quality control, challenges remain, particularly in the area of human-like language production and comprehension. In today’s industry, professional QA tester training is a big worry. Due to the conventional training method’s substantial dependence on senior testers to educate and assist trainees, only two or three trainees may typically obtain appropriate instruction at a time from a single senior QA tester. This limitation affects the scalability of QA training programmers. With the new approach, QA training can be made much more productive and scalable since a single senior QA tester can now educate over 20 new hires at once.