{"url":"/dataset/study-data","name":"Study data","full_name":null,"description_markdown":"# Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study\r\n\r\n## File Descriptions\r\n\r\nFile | Description\r\n--- | ---\r\n`commit_categorizations.csv` | Categorizations for the commits in our dataset.\r\n`commits.csv` | Information for the commits in our dataset\r\n`datasets.csv` | Contains the names and descriptions of our datasets.\r\n`issue_categorizations.csv` | Categorizations for the chosen issues from our dataset.\r\n`issues.csv` | Information for the issues in our dataset.\r\n`pipeline_stages.csv` | DL pipeline stages and their respective descriptions.\r\n`problem_categories.csv` | Problem categories and their respective descriptions.\r\n`problem_causes.csv` | Problem causes and their respective descriptions.\r\n`problem_fixes.csv` | Problem fixes and their respective descriptions.\r\n`problem_symptoms.csv` | Problem symptoms and their respective descriptions.\r\n`studied_subjects_commits.csv` | Project data for commits.\r\n`studied_subjects_issues.csv` | Project data for issues.\r\n\r\n## Column Descriptions\r\n\r\n### `commit_categorizations.csv`\r\n\r\nColumn | Description\r\n--- | ---\r\n`tf.function related fix?` | `TRUE` when a bug fix related to `tf.function` was found and `FALSE` otherwise. If `FALSE`, subsequent column values will be blank.\r\n`stage` | DL pipeline stage where the problem fix was found.\r\n\r\n### `issue_categorizations.csv`\r\n\r\nColumn | Description\r\n--- | ---\r\n`tf.function related problem?` | `TRUE` when a bug related to `tf.function` was found and `FALSE` otherwise. If `FALSE`, subsequent column values will be blank.\r\n`stage` | DL pipeline stage where the problem was found.\r\n`GH_id` | GitHub issue unique identifier.\r\n\r\n### `issues.csv`\r\n\r\nColumn | Description\r\n--- | ---\r\n`GH_id` | GitHub issue unique identifier.","description_withheld":null,"homepage":"https://doi.org/10.5281/zenodo.5601987","introduced_date":"2022-01-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/challenges-in-migrating-imperative-deep","title":"Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study","first_author":"Tatiana Castro Vélez","url":null},"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Study data"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}