qa_improve/docs/pain-points.md

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last_updated	tags	source
2025-07-15	pain-points bottlenecks qa-process yara724	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