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docker__compose-6410
docker/compose
train
Upgrade `events` to use the new API fields In API version 1.22 the events structure was updated to include labels and better names for fields. We should update to the new field names, and use labels directly from the event, instead of having to query for them with inspect. Ref: https://github.com/docker/docker/pull/1...
[ "compose/cli/log_printer.py", "compose/const.py", "compose/project.py" ]
[ "compose/cli/log_printer.py::watch_events", "compose/const.py::<module>", "compose/project.py::<module>", "compose/project.py::Project.events", "compose/project.py::Project.events.build_container_event" ]
[ { "cid": "compose/cli/log_printer.py", "text": "compose/cli/log_printer.py\nclass LogPresenter\n def __init__(self, prefix_width, color_func)\n def present(self, container, line)\ndef build_log_presenters(service_names, monochrome) # Return an iterable of functions.\n def no_color(text)\ndef max_n...
[ { "cid": "compose/cli/log_printer.py::LogPresenter.__init__", "text": "compose/cli/log_printer.py::LogPresenter.__init__\n def __init__(self, prefix_width, color_func):\n self.prefix_width = prefix_width\n self.color_func = color_func", "label": 0 }, { "cid": "compose/cli/log_pr...
ytdl-org__youtube-dl-1591
ytdl-org/youtube-dl
train
Opus audio conversion failure When trying to extract and convert audio from a youtube video into an opus-encoded audio file I get "ERROR: audio conversion failed:" with no further explanation. Both libopus0 and opus-tools are installed and I have no problems with other codecs like vorbis. I'm not sure if this is becaus...
[ "youtube_dl/PostProcessor.py" ]
[ "youtube_dl/PostProcessor.py::FFmpegExtractAudioPP.run" ]
[ { "cid": "devscripts/transition_helper.py", "text": "devscripts/transition_helper.py", "label": 0 }, { "cid": "setup.py", "text": "setup.py", "label": 0 }, { "cid": "youtube_dl/PostProcessor.py", "text": "youtube_dl/PostProcessor.py\nclass PostProcessor # Post Processor clas...
[ { "cid": "youtube_dl/PostProcessor.py::PostProcessor.__init__", "text": "youtube_dl/PostProcessor.py::PostProcessor.__init__\n def __init__(self, downloader=None):\n self._downloader = downloader", "label": 0 }, { "cid": "youtube_dl/PostProcessor.py::PostProcessor.set_downloader", ...
numpy__numpy-13703
numpy/numpy
train
Dtype.base attribute not documented dtype instances have a `base` attribute that, I think, is meant to be the "base dtype" for subarrays. It is, however, not documented, leading to hesitation with and confusion about its use. See https://stackoverflow.com/questions/52476601/purpose-status-of-the-attribute-numpy-dtype...
[ "numpy/core/_add_newdocs.py" ]
[ "numpy/core/_add_newdocs.py::<module>" ]
[ { "cid": "numpy/core/_add_newdocs.py", "text": "numpy/core/_add_newdocs.py\ndef numeric_type_aliases(aliases)\n def type_aliases_gen()\ndef add_newdoc_for_scalar_type(obj, fixed_aliases, doc)", "label": 1 }, { "cid": "numpy/doc/structured_arrays.py", "text": "numpy/doc/structured_arrays.p...
[ { "cid": "numpy/core/_add_newdocs.py::numeric_type_aliases", "text": "numpy/core/_add_newdocs.py::numeric_type_aliases\ndef numeric_type_aliases(aliases):\n def type_aliases_gen():\n for alias, doc in aliases:\n try:\n alias_type = getattr(_numerictypes, alias)\n ...
wagtail__wagtail-7855
wagtail/wagtail
train
"Use html_url in alias_of API serialisation\nAs per https://github.com/wagtail/wagtail/pull/7669#iss(...TRUNCATED)
[ "wagtail/api/v2/serializers.py" ]
["wagtail/api/v2/serializers.py::<module>","wagtail/api/v2/serializers.py::PageAliasOfField.get_attr(...TRUNCATED)
[{"cid":"docs/conf.py","text":"docs/conf.py\ndef setup(app)","label":0},{"cid":"wagtail/admin/api/se(...TRUNCATED)
[{"cid":"docs/conf.py::setup","text":"docs/conf.py::setup\ndef setup(app):\n app.add_css_file('cs(...TRUNCATED)
googleapis__google-cloud-python-3348
googleapis/google-cloud-python
train
Error reporting system tests needed Follow up to #3263.
[ "error_reporting/nox.py" ]
[ "error_reporting/nox.py::<module>" ]
[{"cid":"bigquery/nox.py","text":"bigquery/nox.py\ndef unit_tests(session, python_version) # Run th(...TRUNCATED)
[{"cid":"bigquery/nox.py::unit_tests","text":"bigquery/nox.py::unit_tests\n@nox.session\n@nox.parame(...TRUNCATED)
Qiskit__qiskit-6240
Qiskit/qiskit
train
"[mpl and latex circuit drawer] The box needs to extend over the classical registers as well when an(...TRUNCATED)
[ "qiskit/visualization/latex.py" ]
["qiskit/visualization/latex.py::QCircuitImage.__init__","qiskit/visualization/latex.py::QCircuitIma(...TRUNCATED)
[{"cid":"qiskit/visualization/circuit_visualization.py","text":"qiskit/visualization/circuit_visuali(...TRUNCATED)
[{"cid":"qiskit/visualization/circuit_visualization.py::circuit_drawer","text":"qiskit/visualization(...TRUNCATED)
pyca__cryptography-8390
pyca/cryptography
train
"S/MIME signature: \"Content-Type: text/plain\" added to message (39.0.0)\nHey,\r\n\r\nI'm observing(...TRUNCATED)
[ "src/cryptography/hazmat/primitives/serialization/pkcs7.py" ]
["src/cryptography/hazmat/primitives/serialization/pkcs7.py::<module>","src/cryptography/hazmat/prim(...TRUNCATED)
[{"cid":"docs/conf.py","text":"docs/conf.py","label":0},{"cid":"docs/development/custom-vectors/secp(...TRUNCATED)
[{"cid":"docs/development/custom-vectors/secp256k1/generate_secp256k1.py::TruncatedHash.__init__","t(...TRUNCATED)
numpy__numpy-11280
numpy/numpy
train
"DOC: doc unclear for np.interp\nhttps://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.h(...TRUNCATED)
[ "numpy/lib/function_base.py" ]
[ "numpy/lib/function_base.py::interp" ]
[{"cid":"numpy/lib/function_base.py","text":"numpy/lib/function_base.py\ndef rot90(m, k=1, axes=(0, (...TRUNCATED)
[{"cid":"numpy/lib/function_base.py::rot90","text":"numpy/lib/function_base.py::rot90\ndef rot90(m, (...TRUNCATED)
numpy__numpy-23700
numpy/numpy
train
"DOC: Indexing of vectors a and b wrong in numpy.outer function documentation\n### Issue with curren(...TRUNCATED)
[ "numpy/core/numeric.py" ]
[ "numpy/core/numeric.py::outer" ]
[{"cid":"numpy/core/numeric.py","text":"numpy/core/numeric.py\ndef _zeros_like_dispatcher(a, dtype=N(...TRUNCATED)
[{"cid":"numpy/core/numeric.py::_zeros_like_dispatcher","text":"numpy/core/numeric.py::_zeros_like_d(...TRUNCATED)
dagster-io__dagster-9901
dagster-io/dagster
train
"DagsterTranslator.codify is missing comment argument from superclass\n## Summary\r\nIf you are impo(...TRUNCATED)
[ "python_modules/libraries/dagstermill/dagstermill/translator.py" ]
[ "python_modules/libraries/dagstermill/dagstermill/translator.py::DagsterTranslator.codify" ]
[{"cid":"examples/docs_snippets/setup.py","text":"examples/docs_snippets/setup.py","label":0},{"cid"(...TRUNCATED)
[{"cid":"python_modules/dagster/dagster/_core/definitions/reconstruct.py::get_ephemeral_repository_n(...TRUNCATED)
End of preview. Expand in Data Studio

PatchPilot fault-localization dataset

Training data for PatchPilot's fault-localization cross-encoder (A1): pairs of a bug report and a code candidate (a file or a function), labelled 1 if the developers' fix edits that candidate.

Source

Built by scripts/build_localization_data.py (seed 42) from three public datasets by Princeton NLP:

Dataset Used for
princeton-nlp/SWE-bench (train split) issue text, gold patch, repository
princeton-nlp/SWE-bench_oracle (train) contents of the files the gold patch edits
princeton-nlp/SWE-bench_bm25_27K (train) contents of BM25-retrieved files (hard negatives)

Only the SWE-bench training split is used. Its 35 repositories are disjoint from the 12 repositories of SWE-bench Lite (our evaluation set).

Leakage check

scripts/check_leakage.py compares every selected training instance, and the whole training split, with all 300 SWE-bench Lite test instances on repository, instance id, normalised issue text and normalised gold patch. Result (results/leakage_check.json): zero overlap on all four keys.

Construction

  1. Selection: at most 400 instances per repository (seeded), so pandas (5,049 instances) does not dominate.
  2. Labels: the gold patch is parsed into edited files and original-file line numbers (insertions are attributed to the preceding line); Python's ast maps those lines to the innermost enclosing function. Test files, non-Python files and newly created files are not localization targets.
  3. Candidates: gold files + BM25-retrieved Python files. Functions: every function of a gold file (positives and in-file hard negatives) and up to 40 functions of each other file.
  4. Text views: a file is its path plus class/function signatures and one-line docstrings (max 3,000 characters); a function is path::Qualified.name plus its source (max 2,000 characters); the query is the issue text (max 4,000 characters).
  5. Splits: by repository (whole repositories go to train, validation or test), so validation and test measure generalisation to unseen codebases.

Size

From results/localization/dataset_stats.json and results/localization/dataset_function_stats.json:

Split Repositories Instances File candidates (positive) Function candidates (positive functions) Instances with a gold function
train 26 6,329 44,665 (15,931) 824,343 (26,794) 5,378
val 5 1,020 6,382 (3,188) 125,239 (4,116) 888
test 4 1,212 7,296 (3,263) 208,766 (6,066) 1,107
  • Validation repositories: huggingface/transformers, open-mmlab/mmdetection, pypa/pip, scipy/scipy, tiangolo/fastapi. Test repositories: apache/airflow, conda/conda, explosion/spaCy, mesonbuild/meson. No repository appears in more than one split.
  • Of the 8,867 selected instances, 101 do not appear in SWE-bench_oracle's train split and 205 edit no existing non-test Python file; the remaining 8,561 were built.
  • Median candidates per instance: 5-6 files and 91-112 functions.
  • Module-level edits: an edited line outside any function (imports, class attributes, new top-level code) is labelled path::<module> (10,268 / 2,073 / 2,000 such labels in train / val / test). No candidate is ever a whole module, so these labels are excluded from function-level scoring (function_targets()); 951 / 132 / 105 instances only edit module-level code and are therefore scored at file level only. The positive_functions field of dataset_stats.json counts these labels too.

Format

{train,val,test}.jsonl.gz, one JSON object per instance:

{"instance_id": "...", "repo": "owner/name", "split": "train", "query": "issue text",
 "gold_files": ["pkg/mod.py"], "gold_units": ["pkg/mod.py::Class.method"],
 "files": [{"cid": "pkg/mod.py", "text": "skeleton", "label": 1}, ...],
 "units": [{"cid": "pkg/mod.py::Class.method", "text": "source", "label": 1}, ...]}

Limitations

  • Python only; issue text only (no failing-test output, unlike PatchPilot's agent at run time).
  • BM25 candidates come from the SWE-bench 27K-token retrieval, so a file's negatives are capped by what fit in that context.
  • Labels mark what the developers edited; a different correct fix could edit other places.

Licence

Derived from SWE-bench (MIT). The underlying code belongs to the respective open-source projects under their own licences.

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