{ "name": "Jira AI Test Case Generator", "nodes": [ { "parameters": { "public": true, "initialMessages": "Hi there! \ud83d\udc4b\nMy name is Abdulrhman. How can I assist you today?", "options": {} }, "type": "@n8n/n8n-nodes-langchain.chatTrigger", "typeVersion": 1.4, "position": [ -1184, -224 ], "id": "96abbcc5-b525-476b-9463-74561e0875cd", "name": "When chat message received" }, { "parameters": { "promptType": "define", "text": "=Analyze the provided Jira user story and generate comprehensive manual test cases.\n\nStory Key:\n{{ $json.storyKey }}\n\nSummary:\n{{ $json.summary }}\n\nDescription:\n{{ $json.description }}", "options": { "systemMessage": "You are a Senior QA Engineer specialized in manual testing.\n\nAnalyze the provided Jira user story and perform a comprehensive QA analysis before generating manual test cases.\n\nAnalyze all available information including:\n- User Story\n- Description\n- Acceptance Criteria\n- Business Rules\n- Preconditions\n- Field Validations\n- UI Requirements\n- Error Messages\n- Edge Cases\n\nIdentify all possible test scenarios based on:\n- Functional behavior\n- Acceptance Criteria\n- Business Rules\n- Validations\n- User inputs\n- System responses\n- Error handling\n- Edge Cases\n- State transitions\n- Data variations\n- Security considerations (when applicable)\n\nGenerate manual test cases for every identified scenario required to achieve complete functional coverage.\n\nEnsure coverage includes:\n- Happy Path\n- Alternate Flows\n- Negative Scenarios\n- Validations\n- Business Rules\n- Error Handling\n- Edge Cases\n- State Transitions\n- Boundary Conditions (when applicable)\n\nGenerate at least one unique manual test case for every identified test scenario.\n\nFor each test case:\n\n- title\n Write a clear verification statement starting with \"Verify\".\n This will be used as the Jira Subtask Summary.\n\n Example:\n Verify that a registered user can log in successfully using a valid email address or phone number and the correct password.\n\n- description\n Write ONLY a short scenario name.\n Examples:\n - Successful login with valid email or phone and password\n - Empty email\n - Empty password\n - Invalid email format\n - Wrong password\n - Locked account\n - Disabled account\n\n- steps\n Return an ordered array of detailed manual execution steps.\n Prefix every step with its number (for example: \"1. Open the login page\", \"2. Enter a valid email address\", \"3. Click Login\"). This formatting is required for better rendering in Jira.\n\n- expectedResult\n Describe the expected system behavior after executing the steps.\n If the expected result contains multiple outcomes, format them as a numbered list (for example: \"1. User is authenticated.\\n2. Home page is displayed.\\n3. User profile is loaded.\"). If there is only one outcome, prefix it with \"1.\".\n\nRules:\n\n- Preserve the original storyKey.\n- Generate only unique test cases.\n- Do not invent requirements that do not exist.\n- Do not return duplicate scenarios.\n- Never leave any field empty.\n- Do not return markdown.\n- Do not wrap the response in code blocks.\n- Return ONLY valid JSON.\n\nReturn exactly this schema:\n\n{\n \"storyKey\": \"\",\n \"testCases\": [\n {\n \"title\": \"\",\n \"description\": \"\",\n \"steps\": [],\n \"expectedResult\": \"\"\n }\n ]\n}" } }, "type": "@n8n/n8n-nodes-langchain.agent", "typeVersion": 3.1, "position": [ -272, -224 ], "id": "c2cfe24f-c9e8-432d-a6d1-915e9b298369", "name": "AI Agent" }, { "parameters": { "modelName": "models/gemini-3.5-flash", "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini", "typeVersion": 1.1, "position": [ -480, 0 ], "id": "7a913fb9-9d21-4184-9fff-68298d144eba", "name": "Google Gemini Chat Model", "credentials": {} }, { "parameters": { "model": "=llama3.1:8b", "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOllama", "typeVersion": 1, "position": [ -272, 16 ], "id": "d4a02c21-cdd4-4685-b9a7-76c2acea00bc", "name": "Ollama Chat Model", "credentials": {} }, { "parameters": { "fieldToSplitOut": "testCases", "options": {} }, "type": "n8n-nodes-base.splitOut", "typeVersion": 1, "position": [ 240, -192 ], "id": "a6bbc7d6-3d65-4660-81c0-978cc8d6fce6", "name": "Split Out" }, { "parameters": { "project": { "__rl": true, "value": "", "mode": "list", "cachedResultName": "" }, "issueType": { "__rl": true, "value": "", "mode": "list", "cachedResultName": "" }, "summary": "={{ $json.title }}", "additionalFields": { "assignee": {}, "description": "=h2. Description\n\n{{$json.description}}\n\nh2. Steps\n\n{{ $json.steps.join('\\n') }}\n\nh2. Expected Result\n\n{{$json.expectedResult}}", "parentIssueKey": "={{ $('Get an issue').first().json.key }}", "reporter": {} } }, "type": "n8n-nodes-base.jira", "typeVersion": 1, "position": [ 464, -176 ], "id": "637fd855-fd75-49bc-9dfa-c94f28f76d91", "name": "Create an issue", "credentials": {} }, { "parameters": { "assignments": { "assignments": [ { "id": "a3d0a9ed-c9a1-492b-b4c6-8eeb7fbeabcd", "name": "=storyKey", "value": "={{\nJSON.parse(\n $json.output.match(/\\{[\\s\\S]*\\}/)[0]\n).storyKey\n}}", "type": "string" }, { "id": "a34d7729-de18-4c29-9090-c47f206e10c4", "name": "testCases", "value": "={{\nJSON.parse(\n $json.output.match(/\\{[\\s\\S]*\\}/)[0]\n).testCases\n}}", "type": "array" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 48, -176 ], "id": "fde2beee-859a-420f-b84d-d3704e286907", "name": "testCases" }, { "parameters": {}, "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow", "typeVersion": 1.4, "position": [ -64, 80 ], "id": "4f9e64e9-30ae-45d7-b3ca-c4902bcc7252", "name": "Simple Memory" }, { "parameters": { "operation": "get", "issueKey": "={{$json.issueKey}}", "additionalFields": {} }, "type": "n8n-nodes-base.jira", "typeVersion": 1, "position": [ -768, -224 ], "id": "41b28944-f016-4843-90cf-c0922b40089f", "name": "Get an issue", "credentials": {} }, { "parameters": { "assignments": { "assignments": [ { "id": "b170f265-bb60-4fea-8897-39054cd776f5", "name": "storyKey", "value": "={{$json.key}}", "type": "string" }, { "id": "c6a20eb3-707c-44f8-bab0-0a3d45c34c66", "name": "summary", "value": "={{$json.fields.summary}}", "type": "string" }, { "id": "6a10e2f9-936f-40f0-9e71-168cb25e6068", "name": "description", "value": "={{$json.fields.description}}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -528, -208 ], "id": "888ed569-53e9-4131-947f-e6f081957794", "name": "Set Story Context" }, { "parameters": { "assignments": { "assignments": [ { "id": "3bcf857c-36ee-4d97-800a-4bf66d041a68", "name": "issueKey", "value": "={{\n($json.chatInput.match(/[A-Z]+-\\d+/) || [])[0]\n}}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -992, -112 ], "id": "2fe72467-6c52-41b4-8cb2-546beeb41dde", "name": "Set Issue Key" } ], "connections": { "When chat message received": { "main": [ [ { "node": "Set Issue Key", "type": "main", "index": 0 } ] ] }, "Google Gemini Chat Model": { "ai_languageModel": [ [ { "node": "AI Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "Ollama Chat Model": { "ai_languageModel": [ [] ] }, "AI Agent": { "main": [ [ { "node": "testCases", "type": "main", "index": 0 } ] ] }, "Split Out": { "main": [ [ { "node": "Create an issue", "type": "main", "index": 0 } ] ] }, "testCases": { "main": [ [ { "node": "Split Out", "type": "main", "index": 0 } ] ] }, "Simple Memory": { "ai_memory": [ [ { "node": "AI Agent", "type": "ai_memory", "index": 0 } ] ] }, "Set Story Context": { "main": [ [ { "node": "AI Agent", "type": "main", "index": 0 } ] ] }, "Get an issue": { "main": [ [ { "node": "Set Story Context", "type": "main", "index": 0 } ] ] }, "Set Issue Key": { "main": [ [ { "node": "Get an issue", "type": "main", "index": 0 } ] ] } }, "active": true, "settings": { "executionOrder": "v1", "binaryMode": "separate", "availableInMCP": false }, "versionId": "7a1ae2b4-e5f7-44f6-b2e8-262c68e238f0", "meta": { "templateCredsSetupCompleted": true }, "nodeGroups": [], "id": "U197wOyrA7gUjC9V", "tags": [] }