CheckerBench: ECNU and Peking University Introduce the First Executable Benchmark for Coding Agents Synthesizing Static-Analysis Checkers

Background and the Problem

Coding agent benchmarks like SWE-bench evaluate episodic patch generation across existing codebases. However, enterprise software security demands proactive defect prevention: synthesizing reusable static-analysis checkers (e.g., Clang-Tidy plugins, CodeQL queries, Semgrep rules) to guard against recurring vulnerability classes. Writing static analyzers requires deep syntactic and semantic comprehension, traversing abstract syntax trees (ASTs), handling cross-file control flows, and iterating against compiler diagnostics. The community has lacked an executable benchmark assessing this advanced capability.

Architecture and How It Works

Researchers from ECNU, SJTU, and Peking University unveil CheckerBench (arXiv:2610.07557), an end-to-end executable benchmark:

1. 300 Real-World Tasks: Derived from 297 CVEs across 167 open-source repositories and 85 CWE vulnerability categories.
2. Five Language Ecosystems: Spans C/C++, Go, Java, Python, and Rust, mitigating single-language bias.
3. Complete Diagnostic Scaffolds: Pairs vulnerable and patched repository commits with pinned compilation containers and starter checker scaffolds.
4. CheckerLab Evaluation Suite: Rebuilds submitted checkers in hermetic environments, quantifying diagnostic contrast between vulnerable and fixed code, patch localization precision, false-positive rates, and tool interaction efficiency.

Benchmarks and Measured Results

Evaluated across 21 model-harness configurations with three independent trials per configuration:

1. 32.30% Mean Pass@1: Current frontier agents struggle with program-analysis synthesis, achieving an average Pass@1 rate of only 32.30%.
2. 45.33% Peak Performance: The top-performing frontier model harness achieves only 45.33%, leaving more than half of the benchmark's vulnerability checkers unsolved.
3. Persistent Bottlenecks: Error analysis identifies high false-positive rates and brittle compiler-feedback loop handling as the primary collapse modes for coding agents.

Getting Started for Developers

CheckerBench and its dataset (GitHub: ahang0712/CheckerBench-Dataset) highlight an architectural shift for DevSecOps teams. Rather than deploying LLMs to inspect every code diff token-by-token, organizations should train agents to synthesize deterministic static analysis rules. Checkers synthesized by agents can run inside native CI/CD pipelines, delivering millisecond-latency vulnerability scanning across millions of lines of enterprise code.