TEST-DRIVEN DEVELOPMENT ENHANCED BY ARTIFICIAL INTELLIGENCE: AN APPROACH TO SMARTER TEST DESIGN AND VALIDATION

Abstract

In order to enhance the caliber, applicability, and maintainability of automated tests, the study investigates the incorporation of Artificial Intelligence (AI) approaches into the Test-Driven Development (TDD) methodology. The time needed for manual test design, the challenge of finding non-trivial edge situations, and the increasing maintenance cost in large codebases sometimes limit the practical adoption of TDD, despite the fact that it is still one of the most well-known approaches in agile software engineering. Therefore, the paper suggests an AI-enhanced TDD strategy that uses machine learning, massive language models, and natural language processing to enable requirement interpretation, test development, defect-prone region discovery, and test-suite optimization. The suggested method, in contrast to fully autonomous testing scenarios, maintains the developer's primary role: AI is viewed as an intelligent assistant that recommends candidate tests, ranks risky scenarios, clarifies coverage gaps, and supports refactoring decisions, while human control over final validation is maintained. The article outlines a workable workflow for implementing the method on an ASP.NET Core password-reset module, specifies the best methods for each stage, and systematizes how AI may be included into the traditional Red-Green-Refactor cycle. Benefits, restrictions, and adoption concerns are discussed along with an organized mapping between TDD stages and AI capabilities. Faster initial test generation, more coverage of border and negative scenarios, improved verification effort prioritization, and reduced long-term maintenance costs are the primary anticipated outcomes of the suggested technique. The research contends that data quality, controllability of generated outputs, and rigorous developer review are critical to the efficacy of AI-enhanced TDD. Researchers and practitioners interested in AI-assisted testing, intelligent software engineering, and modernizing quality-assurance procedures in software projects may find value in the produced results.