UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large
  Language Models for Program Testing
Yifeng He Jiabo Huang Yuyang Rong Yiwen Guo Ethan Wang Hao Chen
The remarkable capability of large language models (LLMs) in generating
high-quality code has drawn increasing attention in the software testing
community. However, existing code LLMs often demonstrate unsatisfactory
capabilities in generating accurate and complete tests since they were trained
on code snippets collected without differentiating between code for testing
purposes and other code. In this paper, we present a large-scale dataset
UniTSyn, which is capable of enhancing the prowess of LLMs for Unit Test
Synthesis. Associating tests with the tested functions is crucial for LLMs to
infer the expected behavior and the logic paths to be verified. By leveraging
Language Server Protocol, UniTSyn achieves the challenging goal of collecting
focal-test pairs without per-project execution setups or per-language
heuristics that tend to be fragile and difficult to scale. It contains 2.7
million focal-test pairs across five mainstream programming languages, making
it possible to be utilized for enhancing the test generation ability of LLMs.
The details of UniTSyn can be found in Table 1. Our experiments demonstrate
that, by building an autoregressive model based on UniTSyn, we can achieve
significant benefits in learning and understanding unit test representations,
resulting in improved generation accuracy and code coverage across all
evaluated programming languages. Code and data will be publicly available.