QA Workflow Assessment (Version 0.1)
1. Profile

Official Job Title

QA Engineer

Experience

Approximately 1 year

Primary Projects

Yara (main project)
TamasLink (Call Center)
Other projects (Balan, Beno, Karjo, etc.) on a periodic basis

Reporting

Work requests come directly from both the CEO and CTO.
2. Nature of the Role

Although the official title is QA Engineer, the actual responsibilities extend far beyond traditional software testing.

The role combines responsibilities from multiple disciplines:

QA Engineer
Requirement Analyst
Product Analyst
Technical Writer
Bug Investigator
API Tester
Database Inspector
3. Requirement Intake

Requirements do not follow a standardized process.

Typical characteristics:

Received from different stakeholders.
Usually communicated verbally or as short informal messages.
Often consist of only one sentence or a rough idea.
Frequently marked as urgent.
Sometimes conflict with existing business workflows.
Require additional clarification before implementation.
4. Requirement Analysis Process

For clear requirements:

Receive Request
        ↓
Open Jira
        ↓
Navigate the current flow
        ↓
Capture screenshots
        ↓
Document the requirement
        ↓
Use ChatGPT with a predefined Jira template
        ↓
Review
        ↓
Publish Jira Task

For ambiguous or larger requirements:

Receive Request
        ↓
Analyze current workflow
        ↓
Discuss with Frontend / Backend
        ↓
Reach verbal agreement
        ↓
Create Jira Task
5. Investigation Workflow

When validating features or investigating bugs, the workflow typically involves several tools.

UI
        ↓
Browser DevTools
        ↓
Network Requests
        ↓
Swagger
        ↓
MongoDB
        ↓
Root Cause Analysis
6. Current Toolset

Daily tools include:

Jira
Swagger
MongoDB
Browser DevTools
Bruno
Postman
JMeter
Paint (UI annotation)

The repositories of the backend and frontend applications are also consulted when necessary, although code-reading skills are still at a beginner level.

7. Current Pain Points
A. API Complexity

The biggest time consumer is API investigation.

Reasons include:

Large number of APIs
Difficulty remembering rarely used APIs
Finding the correct endpoint
Understanding API sequences within a business flow
Authentication before testing
Preparing request bodies
Switching between multiple tools
B. Authentication Overhead

Many test sessions require repeating the authentication process.

Typical sequence:

Open Swagger
        ↓
Captcha API
        ↓
Login API
        ↓
Receive Token
        ↓
Authorize
        ↓
Start Testing

This workflow may be repeated multiple times every day.

C. Stateful Business Flows

The application is heavily state-driven.

Many APIs cannot be tested independently because business entities must first reach a specific state.

Example:

Create Case
      ↓
Upload Documents
      ↓
Expert Review
      ↓
Approval
      ↓
Payment

This creates dependencies between APIs and significantly increases investigation time.

D. Test Data Preparation

MongoDB is frequently used to:

Inspect business entities
Verify system state
Modify test data when necessary
E. Context Switching

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.

8. Current AI Usage

ChatGPT is already integrated into the workflow.

Current use cases include:

Converting raw requirements into structured Jira tickets
Improving wording
Organizing documentation
9. Observations

Based on the current information, the main bottlenecks do not appear to be writing Jira tickets.

Instead, the primary bottlenecks are:

Reconstructing business context.
Finding the correct APIs.
Navigating complex API chains.
Preparing valid test data.
Repeating authentication and setup tasks.
Frequent context switching across tools.
10. Initial Improvement Opportunities

Without changing the product itself, several areas appear promising for future optimization:

Standardized Bruno/Postman collections organized by business flows.
Better use of environments and variables to reduce repetitive authentication work.
Documentation of business flow → API mappings.
Reusable test data and state preparation procedures.
AI-assisted API discovery and workflow navigation.
AI-assisted root cause analysis support.
A centralized QA knowledge base to preserve project knowledge across time and projects.