Automated Testing with AI: Reducing Bugs and Enhancing Software Quality

EasyChair Preprint 13851

Abstract

The integration of Artificial Intelligence (AI) in automated testing has emerged as a transformative approach to enhance software quality and reduce the incidence of bugs. This research explores the effectiveness of AI-driven testing methodologies in identifying and mitigating software defects more efficiently compared to traditional testing techniques. By leveraging machine learning algorithms and predictive analytics, AI can simulate extensive test scenarios, detect patterns, and predict potential failures, thereby ensuring comprehensive test coverage and early bug detection. The study investigates the impact of AI on various stages of software development, including unit testing, integration testing, and regression testing. Empirical results from case studies and industry applications demonstrate significant improvements in test accuracy, speed, and overall software reliability. This research contributes to the growing body of knowledge on AI applications in software engineering, highlighting its potential to revolutionize quality assurance practices.

Keyphrases: AI-driven testing, Predictive Analytics, automated testing, bug reduction, early bug detection., machine learning, quality assurance, software development, software quality, test coverage

@booklet{EasyChair:13851,
  author    = {Adeoye Ibrahim},
  title     = {Automated Testing with AI: Reducing Bugs and Enhancing Software Quality},
  howpublished = {EasyChair Preprint 13851},
  year      = {EasyChair, 2024}}