DiffTester: Accelerating Unit Test Generation for Diffusion LLMs via
  Repetitive Pattern
Authors: name: Lekang Yang, name: Yuetong Liu, name: Yitong Zhang, name: Jia Li

Software development relies heavily on extensive unit testing, which makes
the efficiency of automated Unit Test Generation (UTG) particularly important.
However, most existing LLMs generate test cases one token at a time in each
forward pass, which leads to inefficient UTG. Recently, diffusion LLMs (dLLMs)
have emerged, offering promising parallel generation capabilities and showing
strong potential for efficient UTG. Despite this advantage, their application
to UTG is still constrained by a clear trade-off between efficiency and test
quality, since increasing the number of tokens generated in each step often
causes a sharp decline in the quality of test cases. To overcome this
limitation, we present DiffTester, an acceleration framework specifically
tailored for dLLMs in UTG. The key idea of DiffTester is that unit tests
targeting the same focal method often share repetitive structural patterns. By
dynamically identifying these common patterns through abstract syntax tree
analysis during generation, DiffTester adaptively increases the number of
tokens produced at each step without compromising the quality of the output. To
enable comprehensive evaluation, we extend the original TestEval benchmark,
which was limited to Python, by introducing additional programming languages
including Java and C++. Extensive experiments on three benchmarks with two
representative models show that DiffTester delivers significant acceleration
while preserving test coverage. Moreover, DiffTester generalizes well across
different dLLMs and programming languages, providing a practical and scalable
solution for efficient UTG in software development. Code and data are publicly
available at https://github.com/wellbeingyang/DLM4UTG-open .