SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications?
We introduce SpecGuardBench, a benchmark of 291 tasks from 13 real-world repositories, covering 8 types of verification logic such as functional postconditions and interaction protocols.
ProofPaper ↗
Key points
- As coding agents become more capable, tests remain the primary evidence for judging whether their code is correct.
- Yet passing available tests does not establish that an implementation satisfies its natural language specification.
- Reliable validation requires checks that faithfully capture the specification and scenarios that thoroughly exercise the code, but current benchmarks provide limited insight into whether agents can construct such evidence themselves.
- Given a natural language specification, repository context, and a reference implementation, an agent constructs a validation harness comprising executable checks and test scenarios.
Sources (1)
- [1]SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 02:34 PM
We introduce SpecGuardBench, a benchmark of 291 tasks from 13 real-world repositories, covering 8 types of verification logic such as functional postconditions and interaction protocols.
As coding agents become more capable, tests remain the primary evidence for judging whether their code is correct.
Extractive summary: sentences quoted from the sources.