Synthesizing File-Level Data for Unit Test Generation with Chain-of-Thoughts via Self-Debugging

TL;DR

This paper introduces a novel self-debugging data distillation method to generate high-quality unit tests with faithful chain-of-thought explanations, significantly improving test generation effectiveness for large language models.

Contribution

It proposes a self-debugging guided test repair and CoT compression approach to create a large high-quality dataset for fine-tuning models for unit test generation.

Findings

Achieved a 36.17% test assertion pass rate.

Attained 43.90% branch coverage.

Reached an 88.66% mutation score.

Abstract

Automatic unit test (UT) generation is essential for software quality assurance, but existing approaches--including symbolic execution, search-based approaches, and recent LLM-based generators--struggle to produce human-quality tests with correct, meaningful assertions and reliable chain-of-thought (CoT) explanations. We identify a gap in UT training data: repository-mined tests lack developer CoTs, while LLM-distilled CoTs are often incorrect or incomplete. To address this issue, we propose a novel data-distillation approach that uses self-debugging to produce high-quality UT training examples paired with faithful CoTs. Our approach combines (1) guided test repair, a heuristic loop (error-, failure-, and coverage-focused steps) that asks the used model to diagnose and iteratively fix generated tests, and (2) CoT compression, which compacts original and debugging CoTs into concise explanations that directly justify correct tests. We apply this pipeline to a large corpus of open-source projects to construct a dataset of 74,518 high-quality <focal method, test, CoT> examples, and then use it for supervised fine-tuning of a base model. An empirical evaluation shows that the fine-tuned model achieves high UT generation effectiveness: it attains a pass rate of 36.17% on test assertions, a branch coverage of 43.90%, and a mutation score of 88.66%, substantially higher than state-of-the-art commercial models like o4-mini.