Breaking Barriers in Software Testing: The Power of AI-Driven Automation
This addresses the problem of inefficient software testing for developers and organizations, though it appears incremental as it combines existing AI techniques like NLP and RL into a new framework.
The paper tackles the problem of slow, costly, and incomplete software testing by introducing an AI-driven framework that automates test case generation and validation, resulting in measurable gains in defect detection, reduced testing effort, and faster release cycles.
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.