Code Generation by Differential Test Time Scaling
Test-time scaling has emerged as a promising approach for improving code generation by exploring large solution spaces at inference time. However, existing methods often rely on public test cases that are unavailable in practice, or require extensive LLM inference for candidate selection, leading to significant token consumption and time overhead. We present DiffCodeGen, a novel test-time scaling method for code generation based on coverage-guided differential analysis. DiffCodeGen generates diverse code candidates using various sampling and prompting strategies, then applies coverage-guided fuzzing to synthesize inputs without requiring any existing tests or large language models. By exe...
| Authors | Yifeng He, Ethan Wang, Jicheng Wang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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