Leveraging Agile Test Automation Frameworks with Machine Learning for Improved Test Coverage

EasyChair Preprint 13184

Abstract

In the rapidly evolving landscape of software development, Agile methodologies have become the standard for delivering high-quality software efficiently. One critical aspect of Agile development is test automation, which accelerates the testing process and ensures the timely delivery of robust software products. However, achieving comprehensive test coverage remains a challenge, particularly in complex systems with dynamic requirements.

This paper explores the integration of machine learning techniques into Agile test automation frameworks to enhance test coverage and effectiveness. By leveraging machine learning algorithms, such as neural networks and decision trees, alongside traditional testing approaches, organizations can identify patterns, predict potential areas of failure, and optimize test scenarios dynamically.

Keyphrases: Agile, Automation, test

@booklet{EasyChair:13184,
  author    = {Louis Frank and Saleh Mohamed},
  title     = {Leveraging Agile Test Automation Frameworks with Machine Learning for Improved Test Coverage},
  howpublished = {EasyChair Preprint 13184},
  year      = {EasyChair, 2024}}