| --- |
| 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. |
|
|
