Year: 2024
Type: conference-paper
Abstract: Test automation is crucial for maintaining software quality and efficiency, especially in today's fast-paced development environments. Deep learning, a subset of machine learning, offers promising advancements in automating complex testing processes. This paper explores various techniques of integrating deep learning into test automation, identifies the challenges faced, and discusses the prospects of this technology in enhancing software testing efficiency and effectiveness. Detailed case studies, future prospects, and comprehensive literature reviews are included to provide a thorough understanding of the subject.
Author: Narendar Kumar Ale
Institution: University of the Cumberlands
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4401765574