Papers › CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

20 Sep 2019arXiv:1909.09436archive 2025-07-28

Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt

Semantic code search is the task of retrieving relevant code given a natural language query. While related to other information retrieval tasks, it requires bridging the gap between the language used in code (often abbreviated and highly technical) and natural language more suitable to describe vague concepts and ideas. To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus. The corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and preprocessing associated function documentation. In this article, we describe the methodology used to obtain the corpus and expert labels, as well as a number of simple baseline solutions for the task. We hope that CodeSearchNet Challenge encourages researchers and practitioners to study this interesting task further and will host a competition and leaderboard to track the progress on the challenge. We are also keen on extending CodeSearchNet Challenge to more queries and programming languages in the future.

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Syntology Ran 8 of 17 code samples harvested from 3 repositories linked to this paper; 9 have no recorded run. Of those that ran: 1 ran · honoured contract; 3 ran · our draft was wrong; 4 ran with no contract checked.

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github/CodeSearchNet officialmentioned in papermentioned on GitHubtf report
UmarFarooqui/MethodNamePrediction mentioned on GitHubnot reachable when probed 2026-09-17 — repositories for recent papers often appear after camera-ready report
UmarFarooqui/Springboard mentioned on GitHubnot reachable when probed 2026-09-17 — repositories for recent papers often appear after camera-ready report
aminatadjer/test mentioned on GitHubpytorch report
code-desc/codesc mentioned on GitHubpytorchMIT report
csebuetnlp/CoDesc mentioned on GitHubpytorchMIT report
jmg764/CodeSearchNet-Challenge mentioned on GitHubtfMIT report
mandubian/codenets mentioned on GitHubpytorchNOASSERTION report
microsoft/CodeBERT mentioned on GitHubpytorch report
nchen909/codeattention mentioned on GitHubpytorchMIT report
salesforce/coderl mentioned on GitHubjaxBSD-3-Clause report
sjj0403/GSCS mentioned on GitHubpytorch report
zfj1998/CodeBert-Code2Text mentioned on GitHubpytorch report

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17 samples harvested; 8 ran; 1 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
3ran · our draft was wrong
4ran
9unverified

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coverage_per_language github/CodeSearchNet/src/relevanceeval.py official repository ran · honoured contract MIT (permissive) · b9d46fdb66261637 · report
load_predictions github/CodeSearchNet/src/relevanceeval.py official repository ran · our draft was wrong MIT (permissive) · faa62c1fbe7df43e · report
load_relevances github/CodeSearchNet/src/relevanceeval.py official repository ran · our draft was wrong MIT (permissive) · 9279f17104890350 · report
add_lang_by_task nchen909/codeattention/utils.py community (archive-listed) ran fingerprinted MIT (permissive) · 2e3e72e1e860afb2 · report
compute_bleu nchen909/codeattention/evaluator/bleu.py community (archive-listed) ran MIT (permissive) · 49289a7f94c227bd · report
count_ngrams nchen909/codeattention/evaluator/smooth_bleu.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · 835fca2953749b59 · report
get_model_size nchen909/codeattention/models.py community (archive-listed) ran MIT (permissive) · 8cfe38019e9d6a49 · report
pad_sequence nchen909/codeattention/evaluator/CodeBLEU/utils.py community (archive-listed) ran MIT (permissive) · cd90fe437a24c233 · report
add_args nchen909/codeattention/configs.py community (archive-listed) unverified MIT (permissive) · 91fcc17204abe9ed · report
bulid_or_load_gen_model nchen909/codeattention/models.py community (archive-listed) unverified MIT (permissive) · 62f9216d4bcf84c0 · report
convert_clone_examples_to_features nchen909/codeattention/utils.py community (archive-listed) unverified MIT (permissive) · cfd0293d796a30d2 · report
convert_examples_to_features nchen909/codeattention/utils.py community (archive-listed) unverified MIT (permissive) · fa57c04c2fcf72af · report
get_token_attention nchen909/codeattention/attention.py community (archive-listed) unverified MIT (permissive) · 7b76b21df15d335b · report
label_folds jmg764/CodeSearchNet-Challenge/src/dataextraction/dedup_split.py community (archive-listed) unverified MIT (permissive) · 3db12c135869bd1e · report
sample_data jmg764/CodeSearchNet-Challenge/src/error_analysis.py community (archive-listed) unverified MIT (permissive) · 4071a311ada99ce3 · report
to_highlighted_html jmg764/CodeSearchNet-Challenge/src/error_analysis.py community (archive-listed) unverified MIT (permissive) · 9dbbcd2b21d49a73 · report
tokenize_docstring_from_string jmg764/CodeSearchNet-Challenge/src/dataextraction/utils.py community (archive-listed) unverified MIT (permissive) · 9140bccc6c9f6468 · report

Tasks

4kCode SearchInformation RetrievalNatural Language QueriesRetrieval

Datasets

Introduced by this paper, per the archive.

CodeSearchNet

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