Year: 2024
Type: article
Abstract: Automation testing has rapidly evolved from traditional manual methods to sophisticated AI-driven autonomous testing frameworks. In this paper, we have discussed the evolution of the automation testing role and, in particular, the transition from the SDET, Software Development Engineer in Test, to a fully autonomous testing solution. We look at a set of emerging technologies-artificial intelligence, machine learning, and advanced analytics-enabling the creation of self-healing test scripts, predictive analysis, and real-time feedback loops. Autonomous testing promises not just efficiency but also a more adaptive response to software changes with minimum human intervention as organizations try to move toward quicker release cycles with higher resilience. The emphasis of this paper is on the skills SDETs will require in this new landscape, the technological drive for autonomous testing, and what all this means for the future of quality assurance. We aspire to present insights into how autonomous testing can help improve productivity and cost reduction, thereby improving software reliability and setting milestones in automation for software development. Keywords Automation Testing, Autonomous Testing, SDET (Software Development Engineer in Test), AI in Testing, Machine Learning in Testing, Natural Language Processing (NLP) in Testing.
DOI: 10.55041/ijsrem6802
Author: Swetha Sistla
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404645532