Breaking Barriers in Software Testing: The Power of AI-Driven Automation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912548601724928 |
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| author | Naqvi, Saba Baqar, Mohammad |
| author_facet | Naqvi, Saba Baqar, Mohammad |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_16025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breaking Barriers in Software Testing: The Power of AI-Driven Automation Naqvi, Saba Baqar, Mohammad Software Engineering Artificial Intelligence 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. |
| title | Breaking Barriers in Software Testing: The Power of AI-Driven Automation |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2508.16025 |