Datasets:
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license: other
license_name: per-function-and-mit
pretty_name: CodeSearchNet Challenge, extended
language:
- en
- code
task_categories:
- text-retrieval
tags:
- code-search
- relevance
- llm-as-a-judge
size_categories:
- 10K<n<100K
configs:
- config_name: queries
data_files: queries.jsonl.gz
- config_name: corpus
data_files: corpus.jsonl.gz
- config_name: qrels
data_files: qrels.jsonl.gz
default: true
---
# CodeSearchNet Challenge, extended: every search over every function
The [CodeSearchNet Challenge](https://github.com/github/CodeSearchNet) (Husain et al., 2019) has
99 natural-language code searches, and experts rated a few candidate functions for each. This
dataset treats every rated function of a language as one codebase and searches all of it: for each
search, every function in its language is a candidate. The experts' ratings are kept, and the
pairs they never rated but a search tool returned were rated on the same scale by a judge model,
validated against the experts first. Random samples of the pairs only a loose any-keyword grep
returned, and of the pairs nothing returned, were rated too.
| Language | Searches | Functions | Rated by experts | Rated by the judge | Relevant |
|---|---:|---:|---:|---:|---:|
| Python | 99 | 943 | 956 | 3,857 | 787 |
| Java | 99 | 758 | 769 | 3,561 | 512 |
| JavaScript | 96 | 304 | 305 | 3,004 | 247 |
| PHP | 99 | 288 | 289 | 2,976 | 232 |
| Ruby | 97 | 292 | 300 | 2,814 | 189 |
| Go | 83 | 161 | 162 | 2,133 | 79 |
It was made for the [jevpipe code search benchmark](https://github.com/fabianboth/jevpipe/tree/main/bench),
which describes the method, the judge's validation and the results in full.
## Configurations
- `queries`: `language`, `search_id`, `search` (the text), `split` (`tuning` for the 20 Python
searches used to tune the benchmark's tools, `test` for all others).
- `corpus`: one row per function: `id`, `language`, `code` (the rated lines at the rated commit),
`url`, `repository`, `commit`, `path`, `first_line`, `last_line`, `license` (the repository's
SPDX license id as GitHub reported it in September 2026, `NOASSERTION` or `null`) and `sha256`
of `code`.
- `qrels`: one row per rated search-function pair: `relevance` (the experts' mean rating, else the
judge's rating, 0 to 3), `relevant` (`relevance` of 2 or more), `source` (`expert` or `judge`),
`expert_ratings`, `judge_rating` (also given for expert-rated pairs, to check the judge), and
`rated_because` (`expert`, `pooled`: a search tool returned it, or `sample`: one of the random
samples above).
## How the judge rated
GPT-6 Astra through the Codex CLI, low reasoning effort, no tools, in batches of 40 pairs
mixed across searches, seeing an opaque id, the search and the code (cut at 20,000 characters),
never which tool returned the pair. The prompt gave the Challenge's 0-3 scale and its description of
each level. Before any of its ratings were used, it rated every expert-rated pair of the language;
the gate, set in advance, was an F1 of 0.67 on "relevant" against the experts:
| Language | Expert-rated pairs | Same verdict as the experts | F1 | Gate |
|---|---:|---:|---:|---|
| Python | 956 | 76% | 0.745 | passed |
| Java | 769 | 79% | 0.719 | passed |
| JavaScript | 305 | 83% | 0.781 | passed |
| PHP | 289 | 79% | 0.760 | passed |
| Ruby | 300 | 83% | 0.709 | passed |
| Go | 162 | 80% | 0.629 | **missed** |
Where two experts rated the same pair (848 Python pairs), they agree on 70% of verdicts, F1 0.711. In Go the judge missed the gate on only 40 relevant expert pairs, and calls more pairs relevant (49) than its experts did (40); use its ratings there with that in mind.
Pooled pairs came from four searches: grep with a pattern a coding agent wrote per search, grep
for all of the search's keywords, jevpipe with a probability of 0.3 or more, and DeepSeek V4.1
Flash answering yes or with a probability of 0.3 or more.
## Use it with care
- **Unrated is unknown, not irrelevant.** The judged pairs were found by the three tools above; a
new tool will return relevant functions nobody rated. Treat pairs missing from `qrels` as
unjudged, use a metric that allows for that, or rate the new pairs the same way.
- **Judge ratings are model output**, checked against the experts but not expert ratings.
- **Relevance is from 2019 searches** on public code that may be in any model's training data.
## Licenses
The functions come from the CodeSearchNet corpus, which kept only projects whose license allows
redistributing parts of them. Each function in `corpus` stays under its repository's license,
named in `license` and traceable through `url`. `NOASSERTION` means the repository has a license
file that GitHub cannot match to a standard license; read it in the repository. `null` means GitHub
finds no license in the repository today; CodeSearchNet selected the project in 2019 because its
license then permitted redistribution, and that license still covers the code at the rated commit
that `url` points to. The Challenge's ratings are from its MIT-licensed repository. The searches'
split, the judge ratings and the dataset's structure are released under the MIT license.
## Citation
Husain, H., Wu, H.-H., Gazit, T., Allamanis, M., & Brockschmidt, M. (2019). CodeSearchNet
Challenge: Evaluating the State of Semantic Code Search. arXiv:1909.09436.
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