README.md

metadata

license: mit
tags:
  - code
  - evaluation
  - benchmark
  - coding-agents
  - reliability
  - security
pipeline_tag: text-generation

CodeBench

CodeBench is an evaluation framework for measuring the reliable code generation ability of AI coding agents, beyond inflated pass@k scores.

Key Findings

Finding	Result
H1 — Category error	Standard pass@k treats test-case counts as independent sample counts, producing different scores for agents with identical true reliability
H2 — Score inflation	Current pass@5 ≈ 0.96–0.97 collapses to reliability@5 ≈ 0.00–0.12 when the category error is corrected
H3 — Proxy validity	Single-rollout proxy score has low Spearman correlation with reliability@k; ≥5 rollouts needed for reliable ranking
H4 — Security gap	Security-adjusted reliability@5 is meaningfully lower than reliability@5; leaderboard rank flips when security is accounted for

Metrics

• reliability@k — correct operationalization of Chen et al. (2021) pass@k, using per-(task, agent) rollout counts and binary execution success
• security_adjusted_reliability@k — reliability@k counting only rollouts that are both correct and produce code with no insecure patterns (eval, exec, os.system, yaml.load without Loader, pickle.loads)

Agents Evaluated

Agent	Provider	Model
anote-code	Anthropic	claude-sonnet-4-6 (Anote system prompt)
claude-code	Anthropic	claude-sonnet-4-6
codex	OpenAI	gpt-4o

Figures

Figure 1 — Baseline: pass@1 vs current pass@5

Figure 2 — H1: Category-error proof

Figure 3 — H2: Score inflation magnitude

Figure 4 — H3: Proxy vs reliability@k correlation

Figure 5 — H4: Security-adjusted reliability leaderboard

Datasets

• anote-ai/codebench-tasks — benchmark task definitions
• anote-ai/codebench-results — experiment results (h4_security + swebench_smoke)

Citation

@misc{codebench2026,
  title  = {CodeBench: Measuring Reliable Code Generation},
  author = {Anote AI},
  year   = {2026}
}