Towards Automated Test Adaptation in Fork Ecosystems via Large Language Models

Paper i proceeding, 2026

Fork-based software ecosystems enable independent evolution, but fragment the quality assurance efforts. Although tests developed in one fork are often relevant to others, direct reuse is rare and can be difficult, due to API drift, refactorings, dependency evolution, or other changes. Prior work has shown that many tests are potentially reusable across forks, yet adapting them remains a largely manual task. This paper explores LLM-assisted test case adaptation as a new direction for automating test propagation in fork ecosystems. We propose a compiler-aware, iterative adaptation technique that integrates tests from one fork into another and leverages LLMs to repair incompatibilities revealed by build failures. We report empirical results on Java fork pairs, showing that LLM guidance can successfully adapt tests in 80% of cases where direct reuse fails due to modifications required to the test code itself. We outline research directions toward effective and scalable test propagation.