docs: import qa workflow knowledge from grilling session

docs/pain-points.md

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+---
+last_updated: 2025-07-15
+tags: [pain-points, bottlenecks, qa-process, yara724]
+source: import-knowledge
+---
+
+# Pain Points — Yara724 QA Workflow
+
+## Time Breakdown (estimated)
+
+| Activity | % of Day |
+|---|---|
+| Bug investigation (diagnosis) | 40% |
+| API investigation / test setup | 25% |
+| Creating Jira tickets | 15% |
+| Context switching (tool switching) | 10% |
+| Taking requirements | 5% |
+| Other | 5% |
+
+- [ ] Validate time percentages with actual measurement
+
+## 1. API Investigation (25%)
+
+The biggest time consumer. Reasons:
+
+- 10–20 API calls to prepare a test case from scratch
+- Cases cannot be reused due to backend constraints — must create fresh each time
+- Large number of APIs; difficulty remembering rarely used APIs
+- Finding the correct endpoint
+- Understanding API sequences within a business flow
+- Preparing request bodies
+- Switching between multiple tools
+
+## 2. Authentication Overhead
+
+Many test sessions require repeating the authentication process. Typical sequence:
+
+```
+Open Swagger
+        ↓
+Captcha API
+        ↓
+Login API
+        ↓
+Receive Token
+        ↓
+Authorize
+        ↓
+Start Testing
+```
+
+Repeated ~10 times/day, ~2 minutes each = ~20 min/day pure waste.
+
+## 3. Bug Diagnosis (40%)
+
+Most time-consuming activity. Root cause analysis done via:
+
+- Visual inspection (comparing UI behavior with expected behavior)
+- Swagger doc comparison (sometimes)
+- Reading frontend/backend code (for some bugs)
+
+## 4. Context Switching (10%)
+
+Knowledge is fragmented across multiple systems:
+
+- Jira
+- Swagger
+- DevTools
+- MongoDB
+- Bruno
+- Backend code
+- Frontend code
+- Product knowledge
+
+A single investigation often requires moving repeatedly between these tools.
+
+## 5. Documentation Overhead (15%)
+
+Jira ticket creation takes ~15 min each. Bottleneck is analysis/thinking, not formatting.
+
+## 6. Weak Jira ↔ Code Traceability
+
+- Commit messages rarely reference Jira ticket IDs
+- Folders are disorganized
+
+## 7. Stateful Business Flows
+
+The application is heavily state-driven. Many APIs cannot be tested independently because business entities must first reach a specific state:
+
+```
+Create Case
+      ↓
+Upload Documents
+      ↓
+Expert Review
+      ↓
+Approval
+      ↓
+Payment
+```
+
+This creates dependencies between APIs and significantly increases investigation time.
+
+## 8. Test Data Preparation
+
+MongoDB is frequently used to:
+
+- Inspect business entities
+- Verify system state
+- Modify test data when necessary
+
+## 9. Technical Learning Curve
+
+Background is Mechanical Engineering, not CS. Often uses development tools without fully understanding their best practices:
+
+- Swagger, MongoDB, Postman, Bruno, Browser DevTools, JMeter
+
+Knows how to accomplish tasks, but not always the most efficient or standard way.
+
+## 10. Requirement Quality
+
+Some Jira tickets are created by the CTO using AI. Since the CTO is relatively new to the product, some generated titles and descriptions lack sufficient product context:
+
+- Requirements can become ambiguous
+- Reading and understanding them takes extra effort
+- Additional clarification is often required before implementation
+
+## Primary Bottlenecks (Summary)
+
+The main bottlenecks are NOT writing Jira tickets. Instead:
+
+1. Reconstructing business context
+2. Finding the correct APIs
+3. Navigating complex API chains
+4. Preparing valid test data
+5. Repeating authentication and setup tasks
+6. Frequent context switching across tools