Generative AI for Test Driven Development: Preliminary Results
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Abstract (arXiv)
Test Driven Development (TDD) is one of the major practices of Extreme Programming for which incremental testing and refactoring trigger the code development. TDD has limited adoption in the industry, as it requires more code to be developed and experienced developers. Generative AI (GenAI) may reduce the extra effort imposed by TDD. In this work, we introduce an approach to automatize TDD by embracing GenAI either in a collaborative interaction pattern in which developers create tests and supervise the AI generation during each iteration or a fully-automated pattern in which developers only supervise the AI generation at the end of the iterations. We run an exploratory experiment with ChatGPT in which the interaction patterns are compared with the non-AI TDD regarding test and code quality and development speed. Overall, we found that, for our experiment and settings, GenAI can be efficiently used in TDD, but it requires supervision of the quality of the produced code. In some cases, it can even mislead non-expert developers and propose solutions just for the sake of the query.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 May 2024.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHubcited by 55
- 2026Automated structural testing of LLM-based agents: methods, framework, and case studiescited by 3
- 2026Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skillscited by 0
- 2026Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills10 Sep 2026
- 2026Automated structural testing of LLM-based agents: methods, framework, and case studies25 Jan 2026
- 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub18 Sep 2025