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Abhisek987
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repliedto their post about 6 hours ago
New dataset: 24,922 real Python dependency compatibility experiments🧪 Every experiment installed a pair of Python packages into a fresh environment and tested three stages: resolution → import → runtime smoke test. The finding that motivated this: resolvers only read published metadata. In this data, ~3,000 environments resolved successfully and still failed at import or smoke-test time. That gap is invisible to pip and uv, and this dataset makes it measurable. What's inside: - 21,490 development + 3,432 sealed validation experiments - 41 unique packages, Python 3.8–3.14, Linux x86_64 - The 6 validation packages (boto3, botocore, s3transfer, celery, kombu, billiard) never appear in training — validation measures generalization to entirely unseen packages - Full per-stage logs: exact commands, exit codes, stdout/stderr - Normalized error text + Python exception types for every failure - Wheel metadata with Python/ABI/platform tag compatibility decisions - PEP 751 lockfile hashes - 142 engineered features per experiment, ready for training Outcome labels: pass, resolution_failure, import_failure, smoke_test_failure. I used it to train two models for DepLab (a structured model + a fine-tuned ModernBERT on release notes). On the sealed validation set (entirely unseen packages) the structured model reached 90.0% accuracy and 100% precision when predicting failures. CC BY 4.0. Load it directly: ```python from datasets import load_dataset ds = load_dataset("Abhisek987/deplab-dependency-compatibility", "experiments") ``` Dataset: https://huggingface.co/datasets/Abhisek987/deplab-dependency-compatibility Zenodo DOI: https://doi.org/10.5281/zenodo.21729353 Code: https://github.com/Abhisek12378/DepLab If you're working on ML for developer tools or software engineering research, I'd love to hear what you build with it.
repliedto their post about 19 hours ago
New dataset: 24,922 real Python dependency compatibility experiments🧪 Every experiment installed a pair of Python packages into a fresh environment and tested three stages: resolution → import → runtime smoke test. The finding that motivated this: resolvers only read published metadata. In this data, ~3,000 environments resolved successfully and still failed at import or smoke-test time. That gap is invisible to pip and uv, and this dataset makes it measurable. What's inside: - 21,490 development + 3,432 sealed validation experiments - 41 unique packages, Python 3.8–3.14, Linux x86_64 - The 6 validation packages (boto3, botocore, s3transfer, celery, kombu, billiard) never appear in training — validation measures generalization to entirely unseen packages - Full per-stage logs: exact commands, exit codes, stdout/stderr - Normalized error text + Python exception types for every failure - Wheel metadata with Python/ABI/platform tag compatibility decisions - PEP 751 lockfile hashes - 142 engineered features per experiment, ready for training Outcome labels: pass, resolution_failure, import_failure, smoke_test_failure. I used it to train two models for DepLab (a structured model + a fine-tuned ModernBERT on release notes). On the sealed validation set (entirely unseen packages) the structured model reached 90.0% accuracy and 100% precision when predicting failures. CC BY 4.0. Load it directly: ```python from datasets import load_dataset ds = load_dataset("Abhisek987/deplab-dependency-compatibility", "experiments") ``` Dataset: https://huggingface.co/datasets/Abhisek987/deplab-dependency-compatibility Zenodo DOI: https://doi.org/10.5281/zenodo.21729353 Code: https://github.com/Abhisek12378/DepLab If you're working on ML for developer tools or software engineering research, I'd love to hear what you build with it.
posted an update about 24 hours ago
New dataset: 24,922 real Python dependency compatibility experiments🧪 Every experiment installed a pair of Python packages into a fresh environment and tested three stages: resolution → import → runtime smoke test. The finding that motivated this: resolvers only read published metadata. In this data, ~3,000 environments resolved successfully and still failed at import or smoke-test time. That gap is invisible to pip and uv, and this dataset makes it measurable. What's inside: - 21,490 development + 3,432 sealed validation experiments - 41 unique packages, Python 3.8–3.14, Linux x86_64 - The 6 validation packages (boto3, botocore, s3transfer, celery, kombu, billiard) never appear in training — validation measures generalization to entirely unseen packages - Full per-stage logs: exact commands, exit codes, stdout/stderr - Normalized error text + Python exception types for every failure - Wheel metadata with Python/ABI/platform tag compatibility decisions - PEP 751 lockfile hashes - 142 engineered features per experiment, ready for training Outcome labels: pass, resolution_failure, import_failure, smoke_test_failure. I used it to train two models for DepLab (a structured model + a fine-tuned ModernBERT on release notes). On the sealed validation set (entirely unseen packages) the structured model reached 90.0% accuracy and 100% precision when predicting failures. CC BY 4.0. Load it directly: ```python from datasets import load_dataset ds = load_dataset("Abhisek987/deplab-dependency-compatibility", "experiments") ``` Dataset: https://huggingface.co/datasets/Abhisek987/deplab-dependency-compatibility Zenodo DOI: https://doi.org/10.5281/zenodo.21729353 Code: https://github.com/Abhisek12378/DepLab If you're working on ML for developer tools or software engineering research, I'd love to hear what you build with it.
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