Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for agent-evaluation teams that want to measure whether coding agents can actually handle messy real-world data work, not just code edits.
Who should use it
Who should skip it
Skip ruc-datalab/CoDA-Bench if the source repository or demo is inactive, unmaintained, or no longer matches the description shown here.
About this signal
ruc-datalab/CoDA-Bench is tracked by RepoRadar as a data benchmark in the Research and Evaluation section. It was first seen on 2026-06-30 and last updated on 2026-06-30. The current verdict is 'worth watch' with a Silver tier and advanced setup difficulty. The standout signals for ruc-datalab/CoDA-Bench are open-source/build quality (8.4) and workflow potential (8.2), while setup ease (4.2) trails — that balance shapes where it fits best. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned ruc-datalab/CoDA-Bench a composite score of 7.8 out of 10, placing it in the Silver tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to read AI benchmarks without getting fooled for the checklist behind this score.
Risk explanation
Full runs need a model API key, Docker, and a very large dataset download, so first evaluations should stay on a tiny slice with explicit spend and resource caps; The README examples use forward-looking provider model names, so compare the benchmark contract itself rather than anchoring on any single example model label.