Karate Framework Workflow Visualization with Python | Generated by AI
This Python script uses the networkx and matplotlib libraries to create and visualize a directed graph representing the workflow of the Karate Framework, a popular API testing tool. Here’s a step-by-step explanation of how it works:
1. Setup and Layout
Imports
networkx(nx): A library for creating and manipulating graphs.matplotlib.pyplot(plt): Used for plotting and visualizing the graph.
Layout Function
get_layout(G): Tries to use Graphviz (vianx_agraphornx_pydot) for a clean, hierarchical layout. If Graphviz is not available, it falls back to a spring layout (force-directed algorithm).- Why? Graphviz’s
dotlayout is ideal for directed graphs, as it arranges nodes in a top-down flow.
2. Graph Construction
Create a Directed Graph
G = nx.DiGraph(): Initializes a directed graph.
Nodes
- Nodes represent key components of the Karate Framework workflow (e.g., “Feature files”, “Runner”, “Karate engine”).
- Each node is assigned a category (e.g., “Authoring”, “Execution”, “Runtime”) for coloring and grouping.
Edges
- Edges represent the flow between components, with labels describing the relationship (e.g., “execute”, “invoke”, “call APIs”).
- Example:
"Feature files (.feature)" → "Runner (CLI/JUnit5/Maven/Gradle)"with label “execute”.
3. Visualization
Node and Edge Styling
- Node colors: Each category has a distinct color (e.g., “Authoring” is blue, “Execution” is orange).
- Edge style: Arrows show direction, with labels placed in the middle.
Plotting
nx.draw_networkx_nodes: Draws nodes with specified colors and sizes.nx.draw_networkx_edges: Draws edges with arrows.nx.draw_networkx_labels: Adds node labels.nx.draw_networkx_edge_labels: Adds edge labels.
Legend
- A legend is added to explain the color-coding by category.
Output
- The graph is saved as a PNG file in a
tmpdirectory, with a message printed to confirm the save location.
4. Workflow Representation
The graph visually explains the Karate Framework’s workflow:
- Authoring: Write feature files (
.feature). - Execution: A runner (CLI, JUnit5, Maven, Gradle) executes the tests.
- Runtime: The Karate engine interprets the DSL and performs assertions.
- Protocols: The engine makes HTTP/REST/GraphQL calls to external systems.
- External: External systems/services respond to requests.
- Reporting: Results are generated as reports (HTML, JUnit, JSON) and published to CI/CD pipelines.
Summary Table
| Component | Category | Role in Workflow |
|---|---|---|
| Feature files (.feature) | Authoring | Test scenarios written in Gherkin |
| Runner (CLI/JUnit5/…) | Execution | Executes the test scenarios |
| Karate engine | Runtime | Interprets and runs the tests |
| HTTP/REST/GraphQL | Protocols | Handles API communication |
| External systems | External | Receives requests and sends responses |
| Assertions & matchers | Runtime | Validates responses |
| Reports | Reporting | Generates test reports |
| CI/CD | Reporting | Publishes reports in CI/CD pipelines |
Why This Visualization?
- Clarity: Shows the end-to-end flow of Karate tests.
- Debugging: Helps identify where issues might occur in the pipeline.
- Documentation: Serves as a quick reference for new users.
In short: This script automates the creation of a clear, color-coded workflow diagram for the Karate Framework, making it easier to understand and explain how tests are authored, executed, and reported.