VeriScale: Adversarial Test-Suite Scaling for Verifiable Code Generation
As large language models (LLMs) are increasingly deployed for software engineering, constructing high-quality benchmarks is crucial for evaluating not just the functional correctness, but also the formal verifiability of generated code. However, existing benchmarks are limited by the quantity and quality of positive and negative test cases, leading to an overestimation of model capabilities in generating specifications and implementations. To address this, we propose VeriScale, a novel framework driven by the adversarial implementations. It consists of two stages: test-suite expansion to construct diverse and challenging test cases, and test-suite reduction to distill them into compact ye...
| Authors | Yifan Bai, Xiaoyang Liu, Zihao Mou et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
Connected in the knowledge graph
- XiaoyangLiu-sjtu/VerinaPlusDataset · cited by
- XiaoyangLiu-sjtu/VerinaLiteDataset · cited by
Models and datasets referencing this paper
- XiaoyangLiu-sjtu/VerinaLiteDataset
- XiaoyangLiu-sjtu/VerinaPlusDataset
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