TestingArtifactDetector

This is a command line tool that clones and analyses Git repositories. Its aim is to identify testing artifacts in those repositories, with specialised components for programming languages.

While its functionality could in principle be applied to separate Git repositories, it currently relies on the output of the joss-repo-miner.

Dependencies

This tool was built and tested using Python version 3.12 and pytest version 7.4.4. Other Python dependencies are pandas and the used version was 2.1.4. The tool itself requires a current version of cloc and works with version 1.98.

Installation

After cloning this repo into dir, the installation can be done with

cd dir
pip install -e .

where -e unlocks the "developer mode".

Running

Basic use (one-shot)

To run it on all a CSV file foo/joss_repo_miner_output.csv, you can use the following command

testing-artifact-detector --in-file foo/joss_repo_miner_output.csv --out-file foo/testing_artifact_detector_output.csv --clone-dir bar/

This runs the cloning and analysis on the file foo/joss_repo_miner_output.csv and writes the output to foo/testing_artifact_detector_output.csv. Repositories are respectively cloned into bar as separate directories with their joss_id as directory name.

Further details and options are given by

testing-artifact-detector --help

Advanced use

For bigger data sets (e.g. the whole JOSS corpus), it can make sense to separate the cloning process from the analysis. In this case, the separation can be enforced by calling

testing-artifact-detector --in-file foo/joss_repo_miner_output.csv --out-file foo/testing_artifact_detector_joss_repo_miner_clone_output.csv --clone-dir bar/ --clone-only True

which does only the cloning-step. In the next step

testing-artifact-detector --in-file foo/testing_artifact_detector_joss_repo_miner_clone_output.csv --out-file foo/testing_artifact_detector_joss_repo_miner_output.csv --clone-dir bar/ --assume-cloned True

the cloned repositories are analysed and the final output is generated.

Project Structure:

├── LICENSE
├── pyproject.toml
├── README.md
├── src
│   └── testing_artifact_detector
│       ├── cli.py
│       ├── clone_repo.py # Git cloning infrastructure
│       ├── config_parsers # Scripts for parsing configuration files
│       │   ├── cpp_test_config_parser.py
│       │   ├── __init__.py
│       │   ├── python_test_config_parser.py
│       │   └── r_test_config_parser.py
│       ├── detectors # Scripts for searching for testing artifacts, uses config_parsers
│       │   ├── check_cpp_test_artifacts.py
│       │   ├── check_python_test_artifacts.py
│       │   ├── check_r_test_artifacts.py
│       │   ├── check_test_types.py
│       │   ├── __init__.py
│       │   └── util.py
│       ├── __init__.py
│       ├── __main__.py
│       └── repo_languages.py # cloc based implementation for language analysis
└── test_suite
    ├── __init__.py
    └── unit
        ├── __init__.py
        ├── test_data
        │   ├── config_data
        │   │   ├── Python # Sample test configuration files and source files for Python
        │   │   │   ├── empty_pytest.toml
        │   │   │   ├── pyproject.toml
        │   │   │   ├── pytest.ini
        │   │   │   ├── test
        │   │   │   │   └── test_unit.py
        │   │   │   └── tests
        │   │   │       ├── general_test.py
        │   │   │       ├── invalid_test_file.py
        │   │   │       └── unit
        │   │   │           └── test_unit.py
        │   │   └── R # Sample test configuration files for R
        │   │       ├── multi
        │   │       │   └── DESCRIPTION
        │   │       ├── runit
        │   │       │   └── DESCRIPTION
        │   │       ├── testthat
        │   │       │   └── DESCRIPTION
        │   │       └── tinytest
        │   │           └── DESCRIPTION
        │   └── sample_cloc.json
        ├── test_python_test_artifact_check.py
        ├── test_python_test_config_parsing.py
        ├── test_repo_languages.py
        └── test_r_test_config_parsing.py

Data Validation and Consistency

The tool ensures the following properties:

• it will produce identical results for repeated runs on the same corpus (same input CSV, already cloned repositories).