Evaluating AI-Based Unit Testing Tools: A Focus on Usability, Reliability, and Integration

This study presents an empirical comparison of Artificial Intelligence (AI)-based unit testing tools, focusing on their usability, reliability, and the challenges of integrating them into existing software testing frameworks. As AI-driven testing tools emerge as solutions to the limitations of traditional manual testing, particularly within agile methodologies and continuous integration/continuous deployment (CI/CD) pipelines, their effectiveness remains an area of active research. The research evaluates the potential of these tools to automate test generation and execution, thereby improving test coverage and bug detection while minimizing manual intervention. However, significant usability challenges persist, as developers often find these tools difficult to configure and deploy, especially in projects with varying complexities. Additionally, concerns about the reliability of AI-generated tests, particularly regarding consistent bug detection across different environments, are addressed. Integration into legacy systems poses further obstacles, as many organizations hesitate to overhaul established testing frameworks. The findings of this study offer insights into the current state of AI-based unit testing tools and provide recommendations for overcoming barriers to their widespread adoption in real-world development environments.