The process begins with setting up the environment and installing required dependencies. This step is fundamental for establishing the base upon which the analysis and automation tasks will run.
A common command used for installing dependencies is:
pip install dependencies
This step is often associated with initializing the central server or the primary node of the pipeline. Once the dependencies are installed, it is important to check the console output to confirm successful installation. This stage may be referred to as the dependency installation or initial setup phase.
A key concept discussed is code coverage, which evaluates the extent to which the application's source code is tested.
Code coverage typically applies to files such as app.js, but the principle is relevant across various programming languages (e.g., Python .py, JavaScript .js).
It provides a quantitative measure of how much code is exercised by automated tests.
Achieving 100% coverage means that every line or function in a module is linked to at least one test case.
Importantly, even if a test case fails, the coverage metric remains at 100% ; coverage indicates the presence of testing, not necessarily its success.
Project configuration files like package.json are often involved in managing testing frameworks and dependencies necessary for tracking coverage.
An illustrative test case might be designed to verify a simple function, such as:
"should return hello from app.js".
To perform static code analysis, a dedicated scanning tool must be installed ; commonly referred to as a code scanner or platform application scanner.
Authentication is a necessary step to enable secure communication between the scanner and the analysis server.