MLReproMutate

MLReproMutate is research software for mutation testing of reproducibility-relevant safeguards in machine-learning research software.

It introduces controlled changes to experimental and environment choices and evaluates whether validation workflows already present in a repository detect those changes.

MLReproMutate is intended for empirical software-engineering research, machine-learning reproducibility studies, and developers who want to assess whether existing validation workflows constrain reproducibility-relevant experimental choices.

Installation

MLReproMutate requires Python 3.11 or newer.

Install the current release from PyPI:

python -m pip install mlrepromutate

Confirm the installation:

mlrepromutate version
mlrepromutate --help

For a guided first run, start the interactive setup:

mlrepromutate

The wizard guides you through project selection, mutation operator, validation command, execution mode, candidate preview, and confirmation.

To work from the source repository instead:

git clone https://github.com/ilyuka/MLReproMutate.git
cd MLReproMutate

python -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[dev]"

Quick start

The repository includes small CPU-runnable fixtures demonstrating the mutation workflow.

The random-seed fixture can be run without additional machine-learning dependencies:

mlrepromutate run examples/random-seed \
  --command "python validate_unguarded.py" \
  --operator random-seed \
  --python-file experiment.py

MLReproMutate first validates the unmodified baseline. It then detects the supported mutation candidate, applies the mutation in the selected execution workspace, runs the same validation command, and reports whether the workflow detected the change. The default sandbox execution mode uses temporary isolated project copies.

For the unguarded fixture, the random-seed mutation survives because the validation workflow checks only that the experiment completes successfully.

The same mutation can be evaluated against a workflow containing an explicit reproducibility safeguard:

mlrepromutate run examples/random-seed \
  --command "python validate_guarded.py" \
  --operator random-seed \
  --python-file experiment.py

For this fixture, the mutation is killed because the guarded workflow checks the deterministic output associated with the original seed.

See the quick-start guide for the complete walkthrough.

Mutation operators

The current release implements four reproducibility-relevant mutation classes.

random-seed

Changes a supported literal random seed from N to N + 1.

Supported seed-setting forms include calls such as:

random.seed(42)
np.random.seed(42)
numpy.random.seed(42)
torch.manual_seed(42)

dependency-pin

Relaxes an exact dependency constraint:

package==version

to:

package>=version

The resolved-dependency evaluation mode can additionally determine whether the changed specification actually resolves to a different installed version of the target dependency.

data-split

Changes a supported train_test_split call containing an explicit non-None stratify argument:

stratify=<expression>

to:

stratify=None

cv-fold-count

Changes an explicit literal cross-validation fold count from N to N + 1.

Supported splitter classes include KFold, StratifiedKFold, RepeatedKFold, and RepeatedStratifiedKFold.

Evaluation model

Mutation evaluation is baseline-first.

The unmodified project is evaluated before any mutation outcome is interpreted. A baseline failure or timeout is therefore kept separate from a mutation result.

After a successful baseline, selected mutations are evaluated in isolated project workspaces using the same validation command.

At the execution level:

• KILLED means that the selected validation workflow returned a non-zero status after the mutation was applied.
• SURVIVED means that the selected validation workflow completed successfully after the mutation was applied.
• baseline failures and validation timeouts are represented separately.
• additional semantic states are used where necessary, including dependency equivalence handling during resolved evaluation.

A survived mutation does not by itself establish that a repository or its scientific results are irreproducible. It shows only that the selected validation workflow did not detect that particular controlled change.

Candidate preview

Mutation candidates can be inspected without executing project code:

mlrepromutate detect examples/random-seed \
  --operator random-seed \
  --python-file experiment.py

detect performs candidate detection only. It does not run the baseline, validation command, or mutants.

Execution modes

The default sandbox mode evaluates the project in temporary copies so that mutation targets in the original project are not modified:

mlrepromutate run PROJECT \
  --operator random-seed \
  --command "pytest -q" \
  --execution-mode sandbox

Large directories that are not needed by the validation workflow can be omitted from sandbox copies with repeatable project-relative --exclude options:

mlrepromutate run PROJECT \
  --operator random-seed \
  --command "pytest -q" \
  --execution-mode sandbox \
  --exclude data \
  --exclude checkpoints

Exclusion paths are interpreted relative to the project root. Absolute paths and parent-directory traversal are rejected.

The in-place mode avoids copying the project and is intended for disposable or version-controlled workspaces such as CI checkouts:

mlrepromutate run PROJECT \
  --operator random-seed \
  --command "pytest -q" \
  --execution-mode in-place

MLReproMutate restores its mutation target after each in-place evaluation, including failed or timed-out validations. However, arbitrary side effects created by the validation command itself are not reverted. For that reason, in-place should be used only in workspaces where such side effects are safe.

dependency-pin --dependency-mode resolved currently requires sandbox mode.

Continuous integration

MLReproMutate can be used as a CI validation step. A disposable CI checkout is a natural fit for in-place execution because no full project copy is needed:

- uses: actions/checkout@v4

- name: Install MLReproMutate
  run: python3 -m pip install mlrepromutate

- name: Check reproducibility safeguards
  run: |
    mlrepromutate run . \
      --operator random-seed \
      --python-file experiment.py \
      --execution-mode in-place \
      --command "pytest -q"

For human use, mlrepromutate provides an interactive setup. For scripts, CI, and reproducible research workflows, prefer explicit detect and run commands.

Python validation commands

For validation commands whose executable is exactly python or python3, MLReproMutate resolves the requested executable from PATH and falls back to the other common alias when necessary. Other executables such as pytest, bash, and make are not rewritten.

Machine-readable reports

Use --json-out to write a structured report:

mlrepromutate run examples/random-seed \
  --command "python validate_unguarded.py" \
  --operator random-seed \
  --python-file experiment.py \
  --json-out report.json

Reports contain software and project metadata, validation information, mutation candidate metadata, outcomes, and execution details.

Documentation

User documentation:

• Quick start
• Command-line interface
• Examples

Research and design materials:

• Threat model
• Research log
• Related-work notes

Command-line help for the installed version is also available with:

mlrepromutate run --help

Examples

The examples/ directory contains small fixtures for exercising individual mutation concepts and evaluation behavior.

These fixtures are separate from the frozen empirical corpus and are intended for documentation, testing, and software evaluation.

See examples/README.md.

Empirical research

MLReproMutate has been used as the experimental instrument in an empirical study of reproducibility-relevant safeguards in machine-learning research software.

The repository contains the frozen machine-readable evidence underlying that study, including corpus records, restoration evidence, study metadata, generated accounting tables, and provenance information.

The empirical corpus is frozen and is not expanded or modified in response to observed mutation outcomes.

The software release and frozen empirical artifacts are archived on Zenodo:

MLReproMutate v0.1.0 DOI: https://doi.org/10.5281/zenodo.22126120

The accompanying empirical study is available as a preprint:

Ilya Shulepov. Mutation Testing for Reproducibility Safeguards in Machine Learning Research Software: An Empirical Study. arXiv:2608.27100, 2026.

https://arxiv.org/abs/2608.27100

Development

Install the development environment:

python -m pip install -e ".[dev]"

Run the test suite:

python -m pytest -q

Run static checks:

ruff check src tests

The continuous-integration workflow runs the software checks across supported Python versions.

Reporting problems and contributing

Bug reports, usability feedback, research use cases, documentation improvements, and suggestions for reproducibility mutation operators are welcome.

Please use the GitHub issue tracker for reproducible bugs or usability problems:

https://github.com/ilyuka/MLReproMutate/issues

See CONTRIBUTING.md for development and contribution guidelines.

Citation

Citation metadata is provided in CITATION.cff.

The current archived software release can be cited using:

Shulepov, Ilya. MLReproMutate, version 0.1.2. Zenodo, 2026. https://doi.org/10.5281/zenodo.22126119

The frozen empirical study used MLReproMutate v0.1.0 and its associated archived artifacts (10.5281/zenodo.22126120); that version-specific archive remains unchanged.

License

MLReproMutate is released under the MIT License.