| --- |
| license: mit |
| task_categories: |
| - text-generation |
| tags: |
| - code-generation |
| - unit-tests |
| - fuzzing |
| - icml2026 |
| - reproduction |
| pretty_name: FuzzEval unit tests for HumanEval-f / MBPP-f |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # FuzzEval unit tests for HumanEval-f and MBPP-f |
|
|
| Automatically generated unit tests for a reproduction of the ICML 2026 paper |
| **"Towards Functional Correctness of Large Code Models with Selective Generation"** |
| (Jeong, Kim & Park — [arXiv:2505.13553](https://arxiv.org/abs/2505.13553), |
| official repo [trustml-lab/selective-code-generation](https://github.com/trustml-lab/selective-code-generation)). |
|
|
| The paper's *FuzzEval* paradigm replaces a benchmark's handful of hand-written |
| unit tests with hundreds of unit tests obtained by **fuzzing the reference |
| solution**. This dataset is our reconstruction of that step for |
| [openai/openai_humaneval](https://huggingface.co/datasets/openai/openai_humaneval) |
| and [google-research-datasets/mbpp](https://huggingface.co/datasets/google-research-datasets/mbpp) (sanitized). |
|
|
| ## How it was built |
|
|
| For each problem we take the *canonical solution* as the fuzzing target: |
|
|
| 1. **Seed corpus** — argument tuples parsed out of the benchmark's own asserts. |
| 2. **Coverage-guided loop** — inputs are mutated with type-aware operators |
| (integers, floats, strings, lists/tuples/sets, dicts, recursively); |
| a mutant that reaches a new `(line -> line)` arc of the reference is added to |
| the corpus, exactly as a coverage-guided fuzzer such as Atheris would. |
| 3. **Recording** — every input that executes without raising, twice with the |
| same result (determinism check) and whose `repr` round-trips, yields a unit |
| test `(u, v)` with `v = reference(u)`. |
|
|
| Budget per problem: 600 unique tests, 60k executions, 90 s. |
|
|
| ## Contents |
|
|
| `fuzz_tests.jsonl` — one row per problem: |
|
|
| | field | meaning | |
| | --- | --- | |
| | `task_id` | `HumanEval/i` or `MBPP/i` | |
| | `dataset` | `humaneval` / `mbpp` | |
| | `entry_point` | function under test | |
| | `tests` | list of `{input: repr(args tuple), output: repr(value), seed: bool}` | |
| | `arcs` | distinct control-flow arcs covered in the reference | |
| | `n_execs`, `status`, `secs` | fuzzing budget diagnostics | |
|
|
| `seed: true` marks a test whose input came from the benchmark's own asserts — |
| those are the "manual unit tests" used by the paper's `SCG-small` baseline. |
|
|
| ## Stats |
|
|
| * 591 problems attempted, **562 with a full 600-test suite** (153 HumanEval, 409 MBPP) |
| * **337,200 unit tests** total |
| * 26 problems excluded: 3 have no literal seed inputs, 1 has no valid seed |
| execution, and 22 hit the time budget (reference solutions that blow up on |
| mutated inputs — the paper handles these with manual input-constraint |
| post-processing, which we did not do). |
|
|
| ## Reproduce |
|
|
| ```bash |
| python fuzz_tests.py --target 600 --max-execs 60000 --out data/fuzz_tests.jsonl |
| ``` |
|
|
| Code: see the Workspace tab of the reproduction logbook. |
|
|
