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NASA Code Retrieval Benchmark v0.1

Note: This dataset has been superseded by nasa-impact/nasa-science-code-benchmark-v0.1.1, which introduces a hierarchical structure, official Hugging Face dataset configurations, and evaluation by NASA science division. Please use v0.1.1 for new work.

This dataset provides a code retrieval benchmark based on code from 7 programming languages (Python, C, C++, Java, JavaScript, Fortran, and Matlab) sourced from NASA's GitHub repositories. It serves as the held-out test set for evaluating information retrieval models trained on NASA science code. The primary task is to retrieve a relevant code snippet (from the corpus) given a natural language query, which can be a docstring or a code identifier (e.g., function or class name).

Licensing and Intellectual Property

This dataset is released under CC-BY-4.0 and contains only structured metadata and annotations produced by the dataset authors. It does not redistribute original source code from the indexed repositories.

The corpus.jsonl file contains placeholders for original code content rather than the source code itself. This is by design to respect the intellectual property and licensing terms of individual repository owners.

Users who wish to populate the corpus for research purposes may do so by fetching content directly from the source repositories using the replication scripts provided in the companion repository:

👉 NASA-IMPACT/github-code-discovery — see scripts/code_snippet/

Please ensure you comply with the licensing terms of each individual repository when using the fetched content.

Related Resources

Source Data & Training

This benchmark is the official test set corresponding to the following training and validation datasets.


Dataset Statistics

  • Total unique corpus entries: 117,950
  • Total unique query entries: 119,720

Dataset Structure

The dataset follows a standard Information Retrieval format with three main components: a corpus, a set of queries, and query-relevance judgments (qrels).

Data Fields

  • corpus.jsonl: A collection of all unique code snippets (functions and classes) from all languages.

    • _id: A unique string identifier for the code snippet.
    • text: ⚠️ Placeholder — use replication scripts to populate with original source code.
  • queries.jsonl: A collection of all unique queries (docstrings and identifiers).

    • _id: A unique string identifier for the query.
    • text: The natural language query.
  • qrels/: A directory containing Tab-Separated Values (TSV) files that map queries to their relevant code snippets. This is the ground truth for evaluation. Each file has the format query-id corpus-id score.

Qrels (Query-Relevance Pairs)

The dataset provides several qrels files to evaluate performance across different programming languages and query types.

By Programming Language

Qrels File Description Size
python.tsv All query-corpus pairs for the Python dataset. 64,110
c.tsv All query-corpus pairs for the C dataset. 17,149
c++.tsv All query-corpus pairs for the C++ dataset. 14,975
java.tsv All query-corpus pairs for the Java dataset. 14,088
javascript.tsv All query-corpus pairs for the JavaScript dataset. 5,159
fortran.tsv All query-corpus pairs for the Fortran dataset. 3,586
matlab.tsv All query-corpus pairs for the Matlab dataset. 653

By Query Type

Qrels File Description Size
nasa_science_function_code_docstring_heldout.tsv Pairs where the query is a function docstring. 61,083
nasa_science_function_code_identifier_heldout.tsv Pairs where the query is a function name (identifier). 32,742
nasa_science_class_code_docstring_heldout.tsv Pairs where the query is a class docstring. 13,355
nasa_science_class_code_identifier_heldout.tsv Pairs where the query is a class name (identifier). 12,540

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