AI‑based Test Generation
Turn fragmented delivery data into predictable, high‑accuracy tests
DigyAI – TestGen ingests requirements, domain knowledge, historical results, and UI artifacts to generate, select, and tag the right tests—then pushes them back to your tools with real‑time reporting in DigyDashboard.
- Increase accuracy to 60%+
- Make output predictable
- Automate up to 80% of workflow
- Centralized Prompt Layer
- Optimizer
- Secure Data Store
- Optimization
- Trainer
- Feedback Loop
- Edge‑case mining
- External systems: Test/PM/Test‑Mgmt/Code repos
- Test Repository
- Test Mgmt
- PM System
- Code Repo
Platform building blocks
Context + Memory + Reporting — out of the box
DigyIntelli Search
Unifies Jira, Confluence, code, and incidents into one secure, context‑rich search.
DigyContext
Persistent memory across apps and domains for complete, permission‑aware recall.
DigyImageAnalyzer
Turns screenshots, PDFs, and mockups into structured UI data for test generation.
DigyDashboard
Real‑time enterprise reporting with drill‑downs, composite metrics, and release readiness.
DigyDomainAnalyzer
Captures domain & application knowledge at scale to enrich generation accuracy.
Workflow
From requirement to reporting — fully automated
Requirement Entry
- Capture via Jira/Xray/ qTest/ Confluence or other tools.
- Trigger point for the end‑to‑end automated flow.
1
Data Gathering (comprehensive pull)
- Collects stories, epics, links, acceptance criteria.
- Extracts from Confluence/docs; links code‑repo metadata.
- Ensures complete context for test generation.
2
Test Generation
- AI creates baseline BDD/functional/unit cases.
- Modification from historical failures & patterns.
- Selection prioritizes high‑impact, risk‑based cases.
3
Realtime Data Statistics
- Coverage: tests ↔ requirements.
- Adoption: execution & validation of generated tests.
- Code‑gen reusability across modules.
4
Auto‑Tagging
- Feature / module / requirement / release / risk / priority.
5
Push to Back Stores
- Upload to Jira/Xray/qTest, GitHub/Bitbucket, Confluence docs.
6
Workflow Automation
- Generation → Selection → Upload → Tagging → Reporting → Analysis.
- Feedback loop continuously improves precision.
7
Top 3 challenges in LLM test generation
- ~30% accuracy (industry)
- Multi‑source data collection & analysis
- High manual workflow intervention
Why DigyAI
LLM‑only vs. DigyAI – TestGen
| Dimension | LLM‑only | DigyAI – TestGen |
|---|---|---|
Test Selection & Prioritization | Not supported — cannot decide which cases matter most. | Augmented by context (Intelli Search, Domain & Image analyzers), permission‑aware memory (DigyContext), auto‑tagging & selection, and closed‑loop optimization with reporting. |
Test Modification & Refinement | Limited — struggles to adapt or revise without strong prompting. | Augmented by context (Intelli Search, Domain & Image analyzers), permission‑aware memory (DigyContext), auto‑tagging & selection, and closed‑loop optimization with reporting. |
Test Tagging & Classification | Not supported — no inherent ability to tag by module, risk, feature. | Augmented by context (Intelli Search, Domain & Image analyzers), permission‑aware memory (DigyContext), auto‑tagging & selection, and closed‑loop optimization with reporting. |
Existing Tests Reuse | Weak — cannot consistently map or adapt older tests. | Augmented by context (Intelli Search, Domain & Image analyzers), permission‑aware memory (DigyContext), auto‑tagging & selection, and closed‑loop optimization with reporting. |
Domain & Business Context | Limited — often misses compliance or industry rules. | Augmented by context (Intelli Search, Domain & Image analyzers), permission‑aware memory (DigyContext), auto‑tagging & selection, and closed‑loop optimization with reporting. |
Memory / Historical Learning | Missing — no long‑term recall across sprints/projects. | Augmented by context (Intelli Search, Domain & Image analyzers), permission‑aware memory (DigyContext), auto‑tagging & selection, and closed‑loop optimization with reporting. |
Integration
DigyAI × DigyDashboard — live workflow
- Deep product integration
- Unified data capture: requirements, code metadata, run results, incidents.
- Streaming store powers DigyDashboard composite metrics & release readiness.
- Insights (coverage, flakiness, defect detection rate, adoption) feed back to DigyAI to refine generation and selection.
- Coverage vs requirements
- Flaky tests trend
- Release readiness & risk
- Adoption & automation ratio
- Live flow
- Requirements & Artifacts
Jira / Confluence / code / incidents
- DigyAI — Generate & Select
Baseline → refinement → prioritization
- Auto‑Tag & Push
Jira/Xray/qTest, GitHub/Bitbucket
- Execute in CI/CD
Runs & captures results
- Stream to DigyDashboard
Coverage, flakiness, DDP, risk
- Insights & Alerts
Composite metrics, release readiness
- Closed‑loop Feedback
Failures & risks tune generation
Outcomes
Benefits teams see with DigyAI – TestGen
Accuracy to 60%+
Centralized prompts, deterministic selection, and memory raise precision well beyond LLM‑only baselines.
Predictable output
Tagging by feature/module/risk + feedback loop produces repeatable, auditable results.
Up to 80% workflow automation
Generate → refine → tag → push → report with minimal human orchestration.
Workflow
Bring AI accuracy and reporting to your SDLC
We’ll plug into your Jira / Xray / qTest, code repos, and Confluence to show end‑to‑end generation, selection, and reporting.
