{"url":"/dataset/coverageeval","name":"CoverageEval","full_name":null,"description_markdown":"**CoverageEval** is a dataset specifically designed for evaluating LLMs on this task. To create CoverageEval, we parse the code coverage logs generated during the execution of the test cases. This parsing step enables us to extract the relevant coverage annotations. We then carefully structure and export the dataset in a format that facilitates its use and evaluation by researchers and practitioners alike.","description_withheld":null,"homepage":"https://github.com/microsoft/coverage-eval","introduced_date":"2023-07-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/predicting-code-coverage-without-execution","title":"Predicting Code Coverage without Execution","first_author":"Michele Tufano","url":null},"license":{"name":"MIT license","url":"https://github.com/microsoft/coverage-eval/blob/main/LICENSE"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[],"languages":[],"variants":["CoverageEval"],"data_loaders":[],"num_papers_in_archive":1,"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."}