Enhancing Cloud-Based Regression Testing: Leveraging Machine Learning for Swift and Effective Release Management

EasyChair Preprint 15014

Abstract

In the evolving landscape of software development, the demand for rapid and reliable release cycles necessitates advanced methodologies for regression testing. This article explores innovative approaches to cloud-based regression testing through the integration of machine learning techniques. We examine how leveraging machine learning can significantly enhance the efficiency and effectiveness of regression testing processes. By harnessing cloud infrastructure, organizations can achieve scalable, automated testing solutions that adapt to the dynamic nature of modern software development. The article provides a comprehensive analysis of various machine learning models and algorithms, demonstrating their application in predicting test outcomes, identifying potential failure points, and optimizing test suite selection. Through empirical studies and case examples, we illustrate how these advanced methods contribute to faster release management and improved software quality. Our findings highlight the transformative potential of combining cloud-based environments with machine learning to address the complexities of regression testing in contemporary software engineering.

Keyphrases: Contemporary, Engineering, Regression, cloud-based, complexities, environments, learning, machine, software

@booklet{EasyChair:15014,
  author    = {Anthony Collins},
  title     = {Enhancing Cloud-Based Regression Testing: Leveraging Machine Learning for Swift and Effective Release Management},
  howpublished = {EasyChair Preprint 15014},
  year      = {EasyChair, 2024}}