analyze_test_cases_duplicates_semantic
Identify duplicate test cases using semantic analysis and step clustering to optimize test suites and reduce redundancy in quality assurance workflows.
Instructions
🧠 Advanced semantic duplicate analysis using LLM-powered step clustering and two-phase analysis
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| analysis_mode | No | Analysis mode: basic (fast), semantic (LLM-powered), hybrid (both) | hybrid |
| format | No | Output format | markdown |
| include_clickable_links | No | Include clickable links to Zebrunner web UI (markdown format only) | |
| include_semantic_insights | No | Generate semantic insights about workflows and patterns | |
| include_similarity_matrix | No | Include detailed similarity matrix in output | |
| project_key | Yes | Project key (e.g., 'ANDROID', 'IOS') | |
| similarity_threshold | No | Test case similarity threshold percentage (50-100, default: 80) | |
| step_clustering_threshold | No | Step clustering threshold percentage (50-100, default: 85) | |
| suite_id | No | Optional: Analyze specific test suite ID | |
| test_case_keys | No | Optional: Analyze specific test case keys instead of suite | |
| use_medoid_selection | No | Use medoid-based representative selection instead of heuristic | |
| use_step_clustering | No | Enable two-phase clustering (step clusters first, then test case clusters) |
Input Schema (JSON Schema)
{
"properties": {
"analysis_mode": {
"default": "hybrid",
"description": "Analysis mode: basic (fast), semantic (LLM-powered), hybrid (both)",
"enum": [
"basic",
"semantic",
"hybrid"
],
"type": "string"
},
"format": {
"default": "markdown",
"description": "Output format",
"enum": [
"dto",
"json",
"string",
"markdown"
],
"type": "string"
},
"include_clickable_links": {
"default": false,
"description": "Include clickable links to Zebrunner web UI (markdown format only)",
"type": "boolean"
},
"include_semantic_insights": {
"default": true,
"description": "Generate semantic insights about workflows and patterns",
"type": "boolean"
},
"include_similarity_matrix": {
"default": false,
"description": "Include detailed similarity matrix in output",
"type": "boolean"
},
"project_key": {
"description": "Project key (e.g., 'ANDROID', 'IOS')",
"minLength": 1,
"type": "string"
},
"similarity_threshold": {
"default": 80,
"description": "Test case similarity threshold percentage (50-100, default: 80)",
"maximum": 100,
"minimum": 50,
"type": "number"
},
"step_clustering_threshold": {
"default": 85,
"description": "Step clustering threshold percentage (50-100, default: 85)",
"maximum": 100,
"minimum": 50,
"type": "number"
},
"suite_id": {
"description": "Optional: Analyze specific test suite ID",
"type": "number"
},
"test_case_keys": {
"description": "Optional: Analyze specific test case keys instead of suite",
"items": {
"type": "string"
},
"type": "array"
},
"use_medoid_selection": {
"default": true,
"description": "Use medoid-based representative selection instead of heuristic",
"type": "boolean"
},
"use_step_clustering": {
"default": true,
"description": "Enable two-phase clustering (step clusters first, then test case clusters)",
"type": "boolean"
}
},
"required": [
"project_key"
],
"type": "object"
}