88 lines
3.2 KiB
Markdown
88 lines
3.2 KiB
Markdown
---
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last_updated: 2025-07-15
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tags: [improvement, goals, priorities, yara724]
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source: import-knowledge
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---
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# Improvement Goals — Yara724 QA Workflow
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## Primary Goal
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Automate repetitive work — addresses the two biggest time sinks (bug diagnosis at 40% and API investigation at 25%).
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## Secondary Goal
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Free time for other projects, improve technical understanding.
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## Long-Term Objective
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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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---
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## Priority 1: State Preparation Automation (FIRST)
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A tool that chains API calls to bring a Case to any target state, supporting:
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- Fresh Case creation each time (default)
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- Reuse of existing Cases from MongoDB (optional)
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- All target states: ready for Expert review, ready for Claim submission, ready for Insurer review, etc.
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- Target states cover the full Blame → Claim lifecycle
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- Interface TBD — options: Bruno collection, standalone script, CLI tool
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- [ ] Define success metrics for Priority 1
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## Priority 2: Bug Diagnosis Assistance (SECOND)
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AI that takes a network request/response (from DevTools) and suggests likely root cause, reducing the 40% bug investigation bucket.
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**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.
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- [ ] Define success metrics for Priority 2
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---
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## Improvement Opportunities (from workflow assessment)
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1. Standardized Bruno/Postman collections organized by business flows
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2. Better use of environments and variables to reduce repetitive authentication work
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3. Documentation of business flow → API mappings
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4. Reusable test data and state preparation procedures
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5. AI-assisted API discovery and workflow navigation
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6. AI-assisted root cause analysis support
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7. A centralized QA knowledge base to preserve project knowledge across time and projects
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---
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## Open Questions (from original working doc)
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### Process Understanding
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- What percentage of time is spent on each activity?
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- Which activities are repetitive?
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- Which activities require human judgment?
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- Which activities can be automated?
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- Which activities can be standardized using templates?
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### Documentation
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- Can screenshots be automatically annotated?
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- Can UI differences be detected automatically?
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- Can Jira tickets be generated directly from screenshots?
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- Can API changes be inferred automatically from Swagger?
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### Backend Analysis
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- Can request/response differences be detected automatically?
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- Can API regressions be detected before manual testing?
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- Can Swagger documentation be compared automatically between versions?
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### Testing
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- Which regression tests should be automated?
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- Which tests should remain manual?
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- Can Bruno/Postman collections generate parts of Jira tickets automatically?
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### AI Integration
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- Can AI generate the first draft of every Jira task?
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- Can AI determine whether a bug belongs to the frontend or backend?
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- Can AI summarize long Jira tickets?
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- Can AI analyze DevTools network traffic?
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- Can AI explain unfamiliar code sections?
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