Benchmarking LLMs for Unit Test Generation from Real-World Functions

TL;DR

This paper introduces ULT, a new challenging benchmark for evaluating large language models' ability to generate unit tests from real-world Python functions, addressing limitations of previous benchmarks.

Contribution

The paper presents ULT, a carefully curated, realistic benchmark with high complexity and minimal data contamination, enabling more accurate assessment of LLMs in unit test generation.

Findings

ULT is more challenging than existing benchmarks.

LLMs achieve lower performance metrics on ULT.

PLT enables analysis of memorization versus reasoning.

Abstract

Recently, large language models (LLMs) have shown great promise in automating unit test generation, significantly reducing the manual effort required by developers. To effectively evaluate the capabilities of LLMs in this domain, it is crucial to have a well-designed benchmark that accurately reflects real-world scenarios and mitigates common pitfalls. Existing LLM test generation benchmarks are limited by two critical drawbacks: data contamination and structurally simple function code. As a result, we often cannot rely on the validity of scientific conclusions drawn from empirical studies using these limited benchmarks. The empirical evidence presented may be biased due to contamination and may fail to generalize beyond toy programs due to structural simplicity. To address these problems, we introduce ULT (UnLeakedTestbench), a new benchmark specifically designed for function-level unit test generation from real-world Python functions. ULT is constructed through a multi-stage curation process that ensures high cyclomatic complexity and mitigates test case contamination. With 3,909 carefully selected function-level tasks, ULT provides a more realistic and challenging evaluation of LLMs' test generation capabilities. We also provide PLT (PreLeakedTestbench), a pair benchmark of ULT with leaked tests designed to enable a controlled analysis of memorization versus reasoning in test generation. Our evaluation results demonstrate that ULT is significantly more challenging. For example, test cases generated by LLMs only achieve 41.32\%, 45.10\%, 30.22\%, and 40.21\% for accuracy, statement coverage, branch coverage, and mutation score on average for all LLMs, respectively. These results are substantially lower than the corresponding metrics on TestEval (91.79\%, 92.18\%, 82.04\%, and 49.69\%) and PLT (47.07\%, 55.13\%, 40.07\%, and 50.80\%).