CONCEPTUAL ANALYSIS article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
This article is part of the Research TopicAdvancing AI-Driven Code Generation and Synthesis: Challenges, Metrics, and Ethical ImplicationsView all 3 articles
The Test Pyramid 2.0: AI-assisted Testing Across the Pyramid
Provisionally accepted
- 1Independent researcher, Seattle, United States
- 2Independent Researcher, Belmont, United States
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Ensuring robust test coverage, high code quality, and a strong security posture are persistent challenges in modern industrial software development, especially as systems grow in complexity and release cycles accelerate with recent Artificial Intelligence (AI) related productivity gains. This paper introduces a conceptual framework, "The Test Pyramid 2.0", which offers a clear and actionable path to integrate the latest advances in AI and DevSecOps principles into engineering workflows to achieve greater efficiency, reduce defect leakage, and create more resilient systems. We examine how AI enhances each layer of the test pyramid through capabilities such as automated test generation, coverage analysis, test data synthesis, anomaly detection, and intelligent UI exploration. In parallel, we embed DevSecOps practices directly into the pyramid by aligning security controls with each testing layer, ranging from static analysis and policy enforcement to dynamic testing, misconfiguration detection, and adversarial simulation. We also explore how AI strengthens these security practices through adaptive learning, risk prioritization, and context-aware detection. Together, these advances create a holistic, AI-augmented, and security-conscious testing strategy that supports the speed of modern development without compromising quality or safety.
Keywords: Software Testing, Quality Assurance, artificial intelligence, DevSecOps, Shift Left, Continuous integration, Security Controls, Test generation
Received: 30 Aug 2025; Accepted: 17 Nov 2025.
Copyright: © 2025 Desai, Singh and Amilkanthwar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Priyank Desai, priyank.desai7@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.