Datasets:

ilintar
/

ClassEval

license: cc-by-nc-4.0
task_categories:
  - text-generation
language:
  - en
tags:
  - code-generation
  - benchmark
  - classeval
pretty_name: ClassEval (modernised)
size_categories:
  - n<1K

ClassEval (modernised)

A lightly patched fork of FudanSELab/ClassEval, the 100-task class-level Python code generation benchmark, fixed so that it still runs correctly on a current Python and a current NumPy.

The benchmark itself is unchanged. Every patch either repairs a test that no implementation could pass, or repairs the reference solution. No task was made easier, no prompt (skeleton) was touched, and nothing about what a model is asked to write has changed.

	upstream	this fork
classes whose reference solution passes all its tests	96 / 100	100 / 100
test methods passed by the reference solutions	2182 / 2196	2196 / 2196

Measured on CPython 3.13 and 3.14, NumPy 2.x, in a network-isolated sandbox.

What was changed

Six edits across five tasks. Schema, task ids and field names are identical to upstream.

Tests that no implementation could pass

ClassEval_17 (CalendarUtil) — a time bomb. get_upcoming_events() filters on event['start_time'] >= datetime.now(), but the test hard-codes a 2024-01-02 event as the "upcoming" one, so the task became unpassable once the wall clock passed that date. The event now sits at datetime.now().year + 1; the companion 2023 event, which must stay in the past, is untouched.
ClassEval_31 (DataStatistics4) — assertEqual on a Pearson correlation coefficient. NumPy 2.x returns 0.9819805060619655 against a recorded 0.9819805060619659: a 4-ULP difference, the same number to 15 significant figures. Now assertAlmostEqual.
ClassEval_48 (IpUtil) — asserted that a reverse DNS lookup of 0.0.0.0 returns 'LAPTOP-2CS86KUM', the dataset author's own machine name. This could never pass on any other computer. The assertion now expects None, which is what the lookup yields when it fails. This is the one patch that genuinely weakens a test: no portable replacement asserting a successful reverse lookup exists.

Reference solutions only (models are unaffected)

ClassEval_51 (KappaCalculator) — two independent NumPy 2.0 breakages: np.mat was removed (now np.asmatrix, at both call sites), and float() on a 1×1 matrix is no longer allowed (now indexes [0, 0] explicitly). The second only surfaces once the first is fixed.
ClassEval_58 (MinesweeperGame) — the reference generate_mine_sweeper_map drew mine coordinates without checking for collisions, so two mines could land on the same cell and the board would contain fewer than k mines. It failed its own test about 30% of the time (measured: 21/30 passes across 30 runs). Now retries on collision, with a guard against looping on a full board.

Known remaining quirks

Not patched, because fixing them would change what the model is shown or is metadata rather than content:

ClassEval_69's methods_info maps merge_pdfs to TestPDFHandler, a fixture-only class with zero test methods. This caps upstream's method-level metric at 409/410 for any submission, including ground truth.
ClassEval_22 / _43 / _46 have malformed skeletons. That is prompt text — patching it would change the task.
ClassEval_44 selects its parser by string (BeautifulSoup(html, 'lxml')), so lxml is a hard dependency that appears in no import statement and is absent from upstream's requirements.txt. Install it or that task drops from 23/23 to 7/23.
ClassEval_69's test imports PdfFileReader from PyPDF2, a name the successor package pypdf removed. PyPDF2 cannot be substituted.

Running it

Reproduced by llama-eval in llama.cpp, which executes generated classes in a sandboxed venv (no Docker) and reports class-level and method-level scores:

python3 llama-eval.py --server http://localhost:8033 --model my-model \
    --dataset classeval --n_predict 4096 --temperature 0

Notes for anyone building their own runner:

Do not execute as uid 0. ClassEval_50 asserts that writing to a chmod 0444 file fails; root bypasses permission bits, so it silently scores 14/16.
Give each task its own working directory — 15 tasks write files, all cwd-relative, and the names collide.
Seed the global RNG before each test if you want reproducible verdicts.
gensim publishes no wheel past cp313. Only two pure-Python APIs are used (utils.decode_htmlentities, matutils.unitvec), so a small shim covers 3.14.
NLTK needs exactly punkt_tab, averaged_perceptron_tagger_eng and wordnet (~33 MB). These are the modern names; the dataset asks for the renamed punkt / averaged_perceptron_tagger.

Licence and attribution

Original work: FudanSELab/ClassEval, from "ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation" (arXiv:2308.01861). Upstream distributes the code under MIT and the data under CC BY-NC 4.0; this modified dataset is redistributed under the same CC BY-NC 4.0 terms.

This is a modified version. For the unmodified benchmark, use FudanSELab/ClassEval.