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🐳 Dataset Curation

How our dataset was curated? How to create the benchmark instances?

To obtain testable real-world repositories from GitHub, we propose a fully automated curation pipeline that utilizes GitHub Actions CI and LLM assistance, eliminating the need for human involvement in benchmark construction.

Github crawling

python -m dibench.curate.crawling --help

  1. Searches GitHub for repositories in star_range for language (10-star batches).

  2. Check each repo for workflows, if found, dump repo instance into JSONL.

Test CI locating

python -m dibench.curate.curate --help
  1. Locate the test CI file
  2. Locate the test job in the CI file
  3. Get the ACT command
  4. Sanitize & Mask
  5. Get the gold patch

Execution verifying

python -m dibench.curate.verify --help

Expected:

  1. Tests Pass when dependencies unmasked
  2. Tests Fail when dependencies masked