Enhancing Agile Software Testing: A Hybrid Approach with TDD and AI-Driven Self-Healing Tests

Abstract

Software testing remains a critical challenge in
modern development environments, particularly as
organizations adopt Agile methodologies with
frequent iterations and continuous delivery
requirements. Traditional testing approaches often
struggle with maintenance overhead and
adaptability issues when applications undergo rapid
changes. This paper introduces a hybrid approach
combining Test-Driven Development (TDD) with
AI-driven self-healing mechanisms to address these
persistent challenges. The proposed framework
leverages artificial intelligence to automatically
detect and repair broken tests, significantly reducing
maintenance efforts while ensuring consistent test
coverage. By incorporating reinforcement learning
for test optimization, the system prioritizes critical
test cases based on historical execution data,
enhancing both efficiency and defect detection
capabilities. Performance evaluation demonstrates
substantial improvements over traditional testing
methods: 29.2% faster test execution, 16.7% higher
defect detection rates, 66.7% reduction in
maintenance effort, and 95.6% improvement in
failure recovery rates. Comparative analysis
confirms the framework's superiority against both
conventional TDD and existing reinforcement
learning-based approaches across all key
performance metrics. The integration with CI/CD
pipelines enables continuous, resilient testing even
as software rapidly evolves, offering a significant
advancement toward fully adaptive automated
testing frameworks for dynamic development
environments.