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Testing and AutomationTypeScriptCloud PlatformTest DataAnalytics

Lyceum

Cloud test-data and measurement analytics platform with integrated standards and traceability system

Problem

Test engineers and validation teams face a common challenge: measurement data lives in scattered spreadsheets, test results are hard to compare over time, and linking test outcomes to requirements is manual and error-prone.

Without a centralized system, you can't easily answer questions like: "Did this product pass all conformance tests?" or "How does this unit's performance compare to the last 100 builds?"

Approach

Lyceum is a cloud platform designed to centralize test data, provide analytics, and maintain traceability between requirements, test cases, and results.

Key Architecture Decisions:

  • Cloud-first: accessible from any test station or manufacturing line
  • API-driven: integrates with existing test automation (APx, Python scripts, LabVIEW)
  • Standards-aware: built-in support for audio test standards (AES, IEC, custom specs)
  • Traceability-first: every test result links back to a requirement and test method

What I Built

Backend (Node.js + TypeScript):

  • RESTful API for test data ingestion and retrieval
  • Postgres database with schemas for projects, test cases, results, and requirements
  • Test result parser that accepts JSON, CSV, and structured logs
  • Query engine for filtering/aggregating results (e.g., "all FFT results for serial numbers X-Y")

Frontend (React + TypeScript):

  • Project dashboard showing pass/fail rates, trends over time
  • Test case library with requirements traceability matrix
  • Result viewer with filtering, charting (time-series, histograms)
  • Standards library for quick reference to conformance specs

Integration Layer:

  • Python SDK for automated test scripts to push results to Lyceum
  • Webhook support for CI-style notifications (e.g., Slack on test failure)

Architecture

Diagram placeholder: Client test stations → Lyceum API → Database + Analytics Engine → Web UI

Test Stations (APx, Python, LabVIEW)
    ↓ (HTTP POST)
Lyceum API (Express + TypeScript)
    ↓
Database (Postgres: projects, tests, results, requirements)
    ↓
Analytics Engine (aggregations, trends, pass/fail logic)
    ↓
Web UI (React: dashboards, traceability, result viewer)

Outcomes

Qualitative Impact:

  • Reduced time to analyze test results from hours (manual spreadsheet review) to minutes (dashboard queries)
  • Enabled historical trending: compare current build to previous 50 builds in seconds
  • Improved traceability: clear mapping from requirement → test case → result for audits
  • Made test data accessible to non-engineers (product managers, support teams)

What worked well:

  • API-first design made integration straightforward
  • Postgres jsonb fields allowed flexible schema for different test types
  • Standards library reduced repetitive lookups

What I'd improve:

  • Add more sophisticated statistical analysis (Cpk, control charts)
  • Build real-time monitoring (live dashboard updates during test runs)
  • Implement multi-tenant support for consulting clients

What's Next

  • Integrate with firmware CI/CD pipelines for automated regression testing
  • Add support for more instrument types (oscilloscopes, power analyzers)
  • Build mobile app for quick result checks from the manufacturing floor
  • Expand standards library to cover more domains (RF, power, EMC)
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