arXiv:2605.15229v2 Announce Type: replace-cross Abstract: Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a described issue. Neither isolates the distinct skill of property-based testing: deriving a semantic invariant from documentation, and then constructing an input-generation strategy precise enough to make a random search reveal the violation. We introduce PBT-Bench, a benchmark of 100 curated property-based testing problems across 40 real Python libraries. Each problem injects one or more sema
The proliferation of AI agents and the increasing complexity of software systems necessitate more robust methods for testing their reliability and correctness.
This benchmark addresses a critical gap in evaluating AI agent capabilities by focusing on property-based testing, which is essential for developing trustworthy and production-ready AI systems.
The introduction of PBT-Bench provides a standardized metric for assessing and improving AI agents' ability to identify semantic invariants and generate effective test cases.
- · AI agent developers
- · Software quality assurance
- · Companies adopting AI in critical systems
- · Developers relying solely on traditional testing methods
- · AI agents unable to adapt to complex testing paradigms
AI agents will become more adept at identifying and preventing complex software bugs before deployment.
This improved testing capability will accelerate the adoption of AI agents in high-stakes environments, such as finance and critical infrastructure.
Higher reliability of AI-driven systems could lead to a significant reduction in software-related incidents and financial losses across industries.
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Read at arXiv cs.AI