---
last_updated: 2025-07-15
tags: [improvement, goals, priorities, yara724]
source: import-knowledge
---

# 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.

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## 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

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## Improvement Opportunities (from workflow assessment)

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

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## 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?