Karate Test Framework Architecture Visualization | Generated by AI
Below is a self-contained Python script that uses NetworkX and Matplotlib to draw a high-level architecture/flow of how the Karate test framework works. It groups components by role (authoring, execution, runtime, protocols/IO, external systems, and reporting), and shows the typical flow from feature files through the engine to protocols and reporting.
Instructions:
- pip install networkx matplotlib
- Optionally install pygraphviz or pydot + graphviz if you want a nicer layout; the script auto-detects and falls back to a spring layout.
Code:
import networkx as nx
import matplotlib.pyplot as plt
# Try to use Graphviz for a cleaner layout if available; fall back to spring_layout otherwise.
def get_layout(G):
try:
# Try PyGraphviz
from networkx.drawing.nx_agraph import graphviz_layout
return graphviz_layout(G, prog="dot")
except Exception:
try:
# Try pydot
from networkx.drawing.nx_pydot import graphviz_layout
return graphviz_layout(G, prog="dot")
except Exception:
# Fallback: spring layout
return nx.spring_layout(G, k=1.2, seed=42)
G = nx.DiGraph()
# Define nodes grouped by category
nodes = {
# Authoring
"Feature files (.feature)": "Authoring",
"Reusable features (call/read)": "Authoring",
"karate-config.js / properties": "Authoring",
"Test data (JSON/CSV)": "Authoring",
# Execution
"Runner (CLI/JUnit5/Maven/Gradle)": "Execution",
"Parallel runner": "Execution",
# Runtime
"Karate engine (DSL interpreter)": "Runtime",
"JS engine": "Runtime",
"Variable/context": "Runtime",
"Assertions & matchers": "Runtime",
# Protocols / IO
"HTTP/REST/SOAP/GraphQL": "Protocols",
"WebSocket": "Protocols",
"UI driver (web)": "Protocols",
"Mock server": "Protocols",
# External
"External systems/services": "External",
# Reporting
"Reports (HTML, JUnit, JSON)": "Reporting",
"CI/CD": "Reporting",
}
# Add nodes with category attribute
for n, cat in nodes.items():
G.add_node(n, category=cat)
# Define edges (u -> v) with optional labels
edges = [
# Authoring to Execution
("Feature files (.feature)", "Runner (CLI/JUnit5/Maven/Gradle)", "execute"),
("karate-config.js / properties", "Runner (CLI/JUnit5/Maven/Gradle)", "configure"),
("Test data (JSON/CSV)", "Feature files (.feature)", "data-driven"),
("Reusable features (call/read)", "Feature files (.feature)", "reuse"),
# Execution to Runtime
("Runner (CLI/JUnit5/Maven/Gradle)", "Parallel runner", "optional"),
("Runner (CLI/JUnit5/Maven/Gradle)", "Karate engine (DSL interpreter)", "invoke"),
("Parallel runner", "Karate engine (DSL interpreter)", "parallelize"),
# Runtime internals
("Karate engine (DSL interpreter)", "JS engine", "script expressions"),
("Karate engine (DSL interpreter)", "Variable/context", "manage state"),
# Engine to protocols
("Karate engine (DSL interpreter)", "HTTP/REST/SOAP/GraphQL", "call APIs"),
("Karate engine (DSL interpreter)", "WebSocket", "send/receive"),
("Karate engine (DSL interpreter)", "UI driver (web)", "drive UI"),
("Karate engine (DSL interpreter)", "Mock server", "start/stub"),
# Protocols to external systems
("HTTP/REST/SOAP/GraphQL", "External systems/services", "requests"),
("WebSocket", "External systems/services", "messages"),
("UI driver (web)", "External systems/services", "browser/app"),
("Mock server", "External systems/services", "simulate"),
# Responses flowing back to engine
("External systems/services", "Karate engine (DSL interpreter)", "responses"),
# Assertions and reporting
("Karate engine (DSL interpreter)", "Assertions & matchers", "verify"),
("Assertions & matchers", "Reports (HTML, JUnit, JSON)", "results"),
("Karate engine (DSL interpreter)", "Reports (HTML, JUnit, JSON)", "runtime logs"),
("Reports (HTML, JUnit, JSON)", "CI/CD", "publish"),
]
# Add edges to graph
for u, v, label in edges:
G.add_edge(u, v, label=label)
# Colors per category
category_colors = {
"Authoring": "#4C78A8",
"Execution": "#F58518",
"Runtime": "#B279A2",
"Protocols": "#54A24B",
"External": "#9A9A9A",
"Reporting": "#E45756",
}
# Build color list for nodes
node_colors = [category_colors[G.nodes[n]["category"]] for n in G.nodes()]
# Compute layout
pos = get_layout(G)
plt.figure(figsize=(14, 10))
# Draw nodes
nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=1600, alpha=0.9, linewidths=1.2, edgecolors="black")
# Draw edges
nx.draw_networkx_edges(G, pos, arrows=True, arrowstyle="-|>", arrowsize=16, width=1.2, connectionstyle="arc3,rad=0.06")
# Draw labels
nx.draw_networkx_labels(G, pos, font_size=9, font_color="white")
# Draw a subset of edge labels to reduce clutter
important_edge_labels = {
(u, v): d["label"]
for u, v, d in G.edges(data=True)
if d["label"] in {"execute", "invoke", "parallelize", "call APIs", "start/stub", "verify", "results", "publish"}
}
nx.draw_networkx_edge_labels(G, pos, edge_labels=important_edge_labels, font_size=8, label_pos=0.5, rotate=False)
# Legend
import matplotlib.patches as mpatches
legend_patches = [mpatches.Patch(color=col, label=cat) for cat, col in category_colors.items()]
plt.legend(handles=legend_patches, loc="lower center", ncol=3, bbox_to_anchor=(0.5, -0.05), frameon=False)
plt.title("How the Karate Test Framework Works (High-Level Flow)", fontsize=14)
plt.axis("off")
plt.tight_layout()
plt.show()
What this diagram conveys:
- Authoring: You write .feature files, can reuse other features, configure via karate-config, and feed data.
- Execution: You run via CLI/JUnit5/Maven/Gradle, optionally in parallel.
- Runtime: The Karate engine interprets the DSL, uses a JS engine for expressions, and manages scenario state.
- Protocols/IO: The engine drives HTTP (REST/SOAP/GraphQL), WebSocket, and UI (via Karate’s driver). It can also start a mock server to stub dependencies.
- External systems: Real services or mocked ones respond.
- Reporting: Assertions produce reports (HTML, JUnit XML, JSON), which your CI/CD can publish.
If you want the diagram tailored to a specific slice (e.g., only API testing, or emphasizing mocks or UI), tell me and I’ll adjust the nodes/edges.