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language:
- en
license: mit
task_categories:
- text-generation
tags:
- code
- java
- unit-testing
- methods2test
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# methods2test_small_cleaned
A **structurally-vacuous-filtered** copy of the `train` split of
[`andstor/methods2test_small`](https://huggingface.co/datasets/andstor/methods2test_small)
(context config `fm+fc+c+m+f+t+tc`, the one actually used to fine-tune models in
[andstor/peft-unit-test-generation-replication-package](https://github.com/andstor/peft-unit-test-generation-replication-package)).
Produced for the investigation in
[lhnam/PEFT — FINDINGS.md](https://github.com) (`FINDINGS.md` §1.2, §4 item 2),
which found that **17.8% of the real fine-tuning targets are structurally
vacuous** (no assertion, empty, or tautological) and hypothesized this is a
driver of the "convergence attractor" collapse seen when fine-tuning code LLMs
for JUnit test generation.
## What changed vs. the original
| Split | Original rows | This dataset | Vacuous rate |
|---|---|---|---|
| `train` | 7,440 | **6,124** (vacuous rows dropped) | 17.7% removed |
| `validation` | 953 | 953 (**unchanged**) | 15.2% (left in, for fair eval_loss) |
| `test` | 1,017 | 1,017 (**unchanged**) | 16.9% (left in) |
Only `train` is filtered. `validation` and `test` are byte-identical to the
source dataset's `fm+fc+c+m+f+t+tc` config — the point of this dataset is to
isolate the effect of *training on* cleaner targets while still measuring
`eval_loss` / benchmark success against the real, unfiltered data distribution.
Filtering only the split a model actually learns from, and leaving evaluation
untouched, is what makes a before/after comparison causally meaningful.
## Filtering method
Each `target` (the reference JUnit test) is classified as vacuous if it does
**not** contain a real, non-tautological `assert*`/`fail`/`verify` call:
```python
ASSERT_RE = re.compile(r"\b(assert\w*|fail|verify\w*)\s*\(", re.IGNORECASE)
TAUTOLOGY_RE = re.compile(
r"assert(true)\s*\(\s*true\s*[,)]|assert(false)\s*\(\s*false\s*[,)]|"
r'assertequals\s*\(\s*([A-Za-z0-9_."\']+)\s*,\s*\3\s*[,)]',
re.IGNORECASE,
)
```
Targets under 15 characters are also treated as vacuous ("empty"). This is the
exact classifier used throughout the source investigation (see
`scripts/filter_vacuous_training_data.py` in the repo above), applied here with
`--mode drop`.
Breakdown of the original `train` split before filtering:
| Label | Count | % |
|---|---|---|
| `has_real_assert` (kept) | 6,124 | 82.3% |
| `no_assert` | 1,278 | 17.2% |
| `tautological_assert` | 25 | 0.3% |
| `empty` | 13 | 0.2% |
| **vacuous total (dropped)** | **1,316** | **17.7%** |
(Matches `FINDINGS.md`'s independently-reported 17.8% to within rounding —
recomputed directly from this dataset's own source parquet.)
## Columns
- `id` (string) — original row id from `andstor/methods2test_small`.
- `source` (string) — the prompt/context (unchanged).
- `target` (string) — the reference JUnit test (the fine-tuning label).
No `weight` column — this is the `drop` variant, not `downweight`. See the
source script if you want a down-weighted variant instead.
## Intended use
Point a fine-tuning run's `TRAIN_DATASET` at this repo (config `default`) in
place of `andstor/methods2test_small` (`fm+fc+c+m+f+t+tc`), keeping everything
else — model, LoRA config, epochs, learning rate, validation split — identical,
to test whether removing the training-time shortcut narrows or removes the
post-fine-tuning "convergence attractor" documented in the source repo's
`FINDINGS.md`. This is one experiment in an ongoing, self-correcting
investigation — see that document for the full methodology, caveats, and
history of revisions before citing any number from this dataset card in a
paper.
## Provenance
- Source dataset: [`andstor/methods2test_small`](https://huggingface.co/datasets/andstor/methods2test_small), config `fm+fc+c+m+f+t+tc`, revision confirmed via that dataset's own commit history.
- Source paper / replication package: [andstor/peft-unit-test-generation-replication-package](https://github.com/andstor/peft-unit-test-generation-replication-package).
- License inherited as MIT from the source dataset.
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