Improvement Goals — Yara724 QA Workflow

Primary Goal

Automate repetitive work — addresses the two biggest time sinks (bug diagnosis at 40% and API investigation at 25%).

Secondary Goal

Free time for other projects, improve technical understanding.

Long-Term Objective

Build an AI-assisted QA workflow that combines product knowledge, API analysis, documentation, testing, and bug investigation into a streamlined process. The goal is to reduce manual effort, improve consistency, and allow focus more on high-value engineering work rather than repetitive documentation.

Priority 1: State Preparation Automation (FIRST)

A tool that chains API calls to bring a Case to any target state, supporting:

Fresh Case creation each time (default)
Reuse of existing Cases from MongoDB (optional)
All target states: ready for Expert review, ready for Claim submission, ready for Insurer review, etc.
Target states cover the full Blame → Claim lifecycle
Interface TBD — options: Bruno collection, standalone script, CLI tool
Define success metrics for Priority 1

Priority 2: Bug Diagnosis Assistance (SECOND)

AI that takes a network request/response (from DevTools) and suggests likely root cause, reducing the 40% bug investigation bucket.

Current bug reporting format: QA pastes a URL + screenshot into Jira. Does not paste full request/response body or expected vs actual comparison — this is the gap AI can fill.

Define success metrics for Priority 2

Improvement Opportunities (from workflow assessment)

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

Open Questions (from original working doc)

Process Understanding

What percentage of time is spent on each activity?
Which activities are repetitive?
Which activities require human judgment?
Which activities can be automated?
Which activities can be standardized using templates?

Documentation

Can screenshots be automatically annotated?
Can UI differences be detected automatically?
Can Jira tickets be generated directly from screenshots?
Can API changes be inferred automatically from Swagger?

Backend Analysis

Can request/response differences be detected automatically?
Can API regressions be detected before manual testing?
Can Swagger documentation be compared automatically between versions?

Testing

Which regression tests should be automated?
Which tests should remain manual?
Can Bruno/Postman collections generate parts of Jira tickets automatically?

AI Integration

Can AI generate the first draft of every Jira task?
Can AI determine whether a bug belongs to the frontend or backend?
Can AI summarize long Jira tickets?
Can AI analyze DevTools network traffic?
Can AI explain unfamiliar code sections?

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