The Test Pyramid 2.0: AI-assisted testing across the pyramid

Area of Science:

• Software Engineering
• Artificial Intelligence
• Cybersecurity

Background:

• Modern software development faces challenges in test coverage, code quality, and security due to increasing complexity and accelerated release cycles.
• Artificial Intelligence (AI) has introduced productivity gains, further intensifying these challenges.

Purpose of the Study:

• To introduce a conceptual framework, "The Test Pyramid 2.0", for integrating AI and DevSecOps into engineering workflows.
• To enhance efficiency, reduce defect leakage, and create more resilient systems.

Main Methods:

• Examining AI's role in enhancing test pyramid layers (automated test generation, coverage analysis, data synthesis, anomaly detection, UI exploration).
• Embedding DevSecOps practices within the pyramid (static analysis, policy enforcement, dynamic testing, misconfiguration detection, adversarial simulation).
• Exploring AI's contribution to strengthening security practices (adaptive learning, risk prioritization, context-aware detection).

Main Results:

• AI-augmented testing strategies improve efficiency and reduce defect leakage.
• Integrated DevSecOps practices enhance the security posture across all testing layers.
• The framework provides a holistic approach to quality and safety in accelerated development.

Conclusions:

• "The Test Pyramid 2.0" offers a clear path to leverage AI and DevSecOps for robust software testing.
• This integrated strategy supports rapid development without compromising quality or safety.
• It enables the creation of more resilient and secure software systems.