Breaking Barriers in Software Testing: The Power of AI-Driven Automation

• URL: http://arxiv.org/abs/2508.16025v1
• Date: Fri, 22 Aug 2025 01:04:50 GMT
• Title: Breaking Barriers in Software Testing: The Power of AI-Driven Automation
• Authors: Saba Naqvi, Mohammad Baqar,
• Abstract summary: This paper presents an AI-driven framework that automates test case generation and validation using natural language processing (NLP), reinforcement learning (RL), and predictive models, embedded within a policy-driven trust and fairness model.<n>Case studies demonstrate measurable gains in defect detection, reduced testing effort, and faster release cycles, showing that AI-enhanced testing improves both efficiency and reliability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software testing remains critical for ensuring reliability, yet traditional approaches are slow, costly, and prone to gaps in coverage. This paper presents an AI-driven framework that automates test case generation and validation using natural language processing (NLP), reinforcement learning (RL), and predictive models, embedded within a policy-driven trust and fairness model. The approach translates natural language requirements into executable tests, continuously optimizes them through learning, and validates outcomes with real-time analysis while mitigating bias. Case studies demonstrate measurable gains in defect detection, reduced testing effort, and faster release cycles, showing that AI-enhanced testing improves both efficiency and reliability. By addressing integration and scalability challenges, the framework illustrates how AI can shift testing from a reactive, manual process to a proactive, adaptive system that strengthens software quality in increasingly complex environments.

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