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SpecGuardBench introduces a critical evaluation of coding agents' ability to generate faithful validation harnesses from natural language specifications, revealing a significant gap between passing existing tests and satisfying original requirements. The study identifies that current agents struggle primarily with constructing checks that accurately reflect the specification rather than merely mirroring reference implementations, highlighting a fundamental limitation in automated verification reliability. This research advances the field of machine learning by addressing the core challenge of ensuring code correctness through rigorous, specification-aligned testing mechanisms.
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SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications?

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arXiv:2610.16870v1 Announce Type: new Abstract: 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. 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. Given a natural language specification, repository context, and a reference implementation, an agent constructs a validation harness comprising executable checks and test scenarios. The checks should reject implementations that violate the specification while accepting those that satisfy it, and the scenarios should thoroughly exercise the code. Across four proprietary and open-weight coding agents, none constructs a harness scored as faithful on the benchmark's implementations for more than 63% of the tasks. The bottleneck lies less in exploring test scenarios than in constructing checks that faithfully reflect the specification. Specifically, the agents show a clear tendency to write checks that mirror the reference implementation rather than the specification, and therefore reject implementations that the specification permits.

Title:SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications?

Abstract: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. 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. Given a natural language specification, repository context, and a reference implementation, an agent constructs a validation harness comprising executable checks and test scenarios. The checks should reject implementations that violate the specification while accepting those that satisfy it, and the scenarios should thoroughly exercise the code. Across four proprietary and open-weight coding agents, none constructs a harness scored as faithful on the benchmark's implementations for more than 63% of the tasks. The bottleneck lies less in exploring test scenarios than in constructing checks that faithfully reflect the specification. Specifically, the agents show a clear tendency to write checks that mirror the reference implementation rather than the specification, and therefore reject implementations that the specification permits.

Computation and Language (cs.CL); Software Engineering (cs.SE)

arXiv:2610.16870 [cs.CL]

(or arXiv:2610.16870v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.16870 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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