gros-gatherer 1.0.0

Software development process data gathering

Software development process data gathering

The Python modules in this repository gather data from different sources that are used by software development teams and projects, as well as control a distributed setup of data gathering. The data gathering modules are part of Grip on Software, a research project involving a larger pipeline where the gathered data is made available for analysis purposes through a MonetDB database setup.

The following systems from software development processes are able to be interacted with using the GROS gatherer modules, focusing on data acquisition:

• Jira
• Git, including additional repository data from GitHub and GitLab
• Azure DevOps/VSTS/TFS, including Git-based data
• Subversion
• Jenkins
• Quality-time
• SonarQube
• BigBoat

There are many ways to use the GROS gatherer, such as manual script usage, Docker images, Jenkins jobs, agent-based Docker compose network isolation, central controller instances and usage in other applications. However, this README.md document focuses on the module installation and development. More thorough documentation on compatibility with versions of data sources, configuration, script overviews and agent-controller APIs is found in the online data-gathering documentation.

Installation

The data gathering modules require Python version 3.8 and higher.

To obtain the latest release version of the module and its dependencies from PyPI, use the following command:

pip install gros-gatherer

We recommend creating a virtual environment to manage your dependencies. Make sure that python runs the Python version in the virtual environment. Otherwise, the dependencies are installed to the system libraries path or the user's Python libraries path if you do not have access to the system libraries.

Configuration

Some modules require the existence of settings and credentials files in the directory from which the script importing the module is run. This path is adjustable with environment variables. For details on configuration, view the documentation.

Development and testing

Most of the modules come with unit tests, while also depending on the correctness of dependencies to provide accurate data from sources (i.e. our unit tests often use mocks in place of the dependencies) and testing the actual system in non-production settings. To run unit tests in this repository, first install the test dependencies with make setup_test which also installs all dependencies for the modules. Then coverage run tests.py provides test results in the output, with XML versions compatible with, e.g., JUnit and SonarQube available in the test-reports/ directory. Detailed information on test coverage is also obtainable after a test run in various report formats, for example:

• coverage report -m for a report on (counts of) statements and branches that were hit and missed in the modules in the output.
• coverage html for a HTML report in the htmlcov/ directory.
• coverage xml -i for an XML output suitable for, e.g., SonarQube.

To perform all the steps except the HTML report, run make coverage. If you do not need XML outputs (each test class writes an XML file by default), then run make test to just report on test successes and failures or make cover to also have the terminal report on statement/branch hits/misses.

GitHub Actions is used to run the unit tests and report on coverage on commits and pull requests. This includes quality gate scans tracked by SonarCloud and Coveralls for coverage history.

The Python scripts and modules conform to code style and typing standards which may be checked using Pylint with make pylint and mypy with make mypy, respectively, after running make setup_analysis to install static code analysis tools. The command for mypy provides potential errors in the output and typing coverage reports in several formats, including XML (compatible with JUnit and SonarQube) in the mypy-report/ directory. To also receive the HTML report, use make mypy_html instead.

Finally, the schemas in the schema/ directory allow validation of certain configuration files as well as all the exported artifacts against the schema. For example, the Jira and Azure DevOps field mapping specifications are able to be checked; see the issue trackers documentation section for an example.

We publish releases to PyPI using make setup_release to install dependencies from requirements-release.txt and make release which performs multiple checks: unit tests, typing, lint and version number consistency. The release files are also published on GitHub and from there are archived on Zenodo. Noteworthy changes to the modules are added to the changelog.

License

Data gathering scripts and modules are licensed under the Apache 2.0 License.