Package Installation Verification And Testing

package-installation-verification-and-testing

Summary

Verify that a relocated or migrated Python package installs correctly in a clean environment and executes core functionality without errors. This skill bridges codebase migration (e.g., to a new GitHub organization) and production readiness by confirming that all dependencies resolve, build succeeds, and representative workflows run correctly.

When to use

A Python package has been relocated to a new repository location, reorganized to conform to new organizational standards (e.g., metabolomics-cloud conventions), or its dependencies, metadata, or CI/CD workflows have been modified. Before declaring the migration complete, verify that the package can be installed from scratch and that canonical workflows (e.g., loading a feature table and running functional prediction) produce valid outputs.

When NOT to use

• The package has not been moved or substantially reorganized; use for initial releases only when establishing a baseline.
• The target environment is not a clean, isolated virtual environment; prior installations or system packages may mask dependency issues.
• Test datasets are not representative of real-world inputs (e.g., too small, missing required columns like m/z or p-value) and cannot validate core workflows.

Inputs

• Source Python package codebase (mummichog-style: setup.py, pyproject.toml, requirements.txt, module structure)
• Target organization standards documentation (e.g., metabolomics-cloud conventions for repository structure, CI/CD)
• Representative test datasets (e.g., feature table with m/z, retention time, p-values; annotation JSON; metabolic model in JSON format)
• Automated test suite (unit and integration tests)

Outputs

• Clean installation confirmation (pip install exit code 0, no unresolved dependencies)
• Successful execution of core workflows on test data (feature table → functional prediction)
• Test suite pass/fail report
• Migration report documenting conformance changes and verification results
• Installation and runtime logs (stdout, stderr, build artifacts)

How to apply

First, audit the source repository structure, setup.py, pyproject.toml, requirements.txt, and .github workflows against target organization standards. Adapt package metadata, module naming, documentation, and CI/CD configurations to conform to those conventions. Create the new repository with the migrated codebase and updated configuration. Install the package in a clean virtual environment using pip install to confirm the build succeeds without errors or missing dependencies. Execute representative core workflows (e.g., loading a feature table with m/z, retention time, and p-values, then running functional prediction against a metabolic model) on test datasets. Validate that all automated tests pass and document all conformance changes and verification outcomes in a migration report.

Related tools

• Python pip (Package manager; installs the migrated package in a clean virtual environment to verify all dependencies resolve and build succeeds.)
• virtualenv (Creates an isolated Python environment to test clean installation without interference from system or prior packages.)
• mummichog (version 3) (The subject package being verified after migration; provides core functionality (feature table input, functional prediction, metabolic network analysis).) — https://github.com/metabolomics-cloud/mummichog
• pytest or unittest (Executes automated test suite to confirm all tests pass post-migration.)

Examples

python3 -m mummichog.main -i tests/ineurons_ttest_1127.tsv -j testneuron -a tests/empCpds_with_annotations.json -d .

Evaluation signals

• pip install in a clean virtual environment exits with code 0 and reports 'Successfully installed [package-name]' with all dependencies resolved.
• Core workflow (e.g., python3 -m mummichog.main -i tests/ineurons_ttest_1127.tsv -j testneuron -a tests/empCpds_with_annotations.json -d .) completes without traceback or import errors.
• Output files are generated and contain expected data structures (result.html, JSON pathway/network analysis objects, no null or malformed entries).
• All automated tests pass: pytest or equivalent test runner reports 0 failures.
• Package metadata (name, version, author, dependencies) matches target organization standards and is correctly read from setup.py or pyproject.toml.

Limitations

• Installation verification in a clean environment does not guarantee correctness on all downstream platforms or with all optional dependencies; test on representative target systems.
• Core workflow verification requires representative test datasets (feature tables with m/z, retention time, p-value; annotation; metabolic models); absence of such datasets limits scope.
• Migration report does not validate scientific accuracy of results, only that code executes without error; downstream validation against gold-standard outputs is separate.
• Compound identifier alignment and formula lookup (especially for user-supplied models) are manual steps outside this skill; the README notes 'a translation module is needed' but does not automate it.

Evidence

• [other] Run installation tests in a clean environment using pip install to verify the package builds without errors.: "Run installation tests in a clean environment using pip install to verify the package builds without errors."
• [other] Execute core Mummichog functionality (e.g., loading feature tables and running functional prediction workflows) to confirm runtime correctness on representative metabolomics data.: "Execute core Mummichog functionality (e.g., loading feature tables and running functional prediction workflows) to confirm runtime correctness on representative metabolomics data."
• [other] Validate that all tests pass and generate a migration report documenting conformance changes and verification results.: "Validate that all tests pass and generate a migration report documenting conformance changes and verification results."
• [other] Adapt package metadata, module naming, documentation structure, and CI/CD workflows to conform to metabolomics-cloud conventions.: "Adapt package metadata, module naming, documentation structure, and CI/CD workflows to conform to metabolomics-cloud conventions."
• [readme] virtualenv env; source env/bin/activate; pip install scipy matplotlib xlsxwriter networkx: "virtualenv env; source env/bin/activate; pip install scipy matplotlib xlsxwriter networkx"