Testing Framework Migration with Large Language Models

Abstract: Python developers rely on two major testing frameworks: \texttt{unittest} and \texttt{Pytest}. While \texttt{Pytest} offers simpler assertions, reusable fixtures, and better interoperability, migrating existing suites from \texttt{unittest} remains a manual and time-consuming process. Automating this migration could substantially reduce effort and accelerate test modernization. In this paper, we investigate the capability of LLMs to automate test framework migrations from \texttt{unittest} to \texttt{Pytest}. We evaluate GPT 4o and Claude Sonnet 4 under three prompting strategies (Zero-shot, One-shot, and Chain-of-Thought) and two temperature settings (0.0 and 1.0). To support this analysis, we first introduce a curated dataset of real-world migrations extracted from the top 100 Python open-source projects. Next, we actually execute the LLM-generated test migrations in their respective test suites. Overall, we find that 51.5% of the LLM-generated test migrations failed, while 48.5% passed. The results suggest that LLMs can accelerate test migration, but there are often caveats. For example, Claude Sonnet 4 exhibited more conservative migrations (e.g., preserving class-based tests and legacy \texttt{unittest} references), while GPT-4o favored more transformations (e.g., to function-based tests). We conclude by discussing multiple implications for practitioners and researchers.