Abstract: Software testing in 2024 has witnessed significant advancements, particularly with the integration of artificial intelligence (AI) and machine learning (ML) into testing frameworks. This manuscript provides a comprehensive analysis of these developments, including experimental evaluations of new testing methodologies and tools. The study introduces Smart Test, a novel AI-driven testing framework, and evaluates its performance through detailed experiments on various software systems. While Smart Test demonstrates notable improvements in testing coverage, defect detection, and efficiency, the paper also addresses its limitations, ethical considerations, and scalability challenges. Additionally, strategies for mitigating bias in AI models are discussed. Finally, recommendations for future research are provided, offering a roadmap for the continued evolution of AI in software testing.
Keywords: Software Testing, Automation, Artificial Intelligence, Machine Learning, Software Engineering, Testing Frameworks, Experimentation, Bias Mitigation
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DOI:
10.17148/IJARCCE.2024.131103
