Leveraging Machine Learning for Enhanced Automated Software Testing in Agile Environments

Abstract

In this paper, we propose a systematic literature review on optimizing automated software testing in Agile Context using machine learning. It researches predictive models for bug detection, optimization of test case execution, and integration of continuous feedback loops. This study attempts to improve the efficiency and effectiveness of software testing processes by using machine learning methods. This work is of interest for researchers and practitioners in either software testing or machine learning communities. It allows for higher quality software faster and can deliver tests faster, enforcing concepts like CI/CD in agile Settings improving accuracy and speed of tests. The research looks at different machine learning algorithms as well and how to apply them into the various phases of the Software testing lifecycle. It also includes integrating these practices into your current agile workflow along with the challenges and opportunities that come with it.