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What We Collect

PanDev Metrics gathers multiple classes of engineering data to analyse and refine delivery processes.

Collected Metrics​

1. Code Metrics​

  • Lines of code – volume of code authored
  • Complexity – cyclomatic complexity, nesting depth
  • Test coverage – percentage of code covered by automated tests
  • Duplication – repeated fragments or copy‑paste blocks
  • Technical debt – code issues that require remediation

2. Time Metrics​

  • Active development time – focused coding and editing
  • Debug time – effort spent diagnosing and fixing defects
  • Refactoring time – improving existing code without changing behaviour
  • Code review time – participation in peer reviews

3. Collaboration Metrics​

  • Commits – frequency and size of commits
  • Code reviews – volume and depth of review activity
  • Collaboration – handovers and pair work between developers
  • Communication – conversations in comments and issues

4. Quality Metrics​

  • Defects – number and type of bugs discovered
  • Fixes – turnaround time for bug resolution
  • Regressions – recurring issues that reappear after being closed
  • Performance – build and test execution times

Collection Channels​

IDE Plugins​

  • IntelliJ IDEA – JetBrains plugin family
  • Visual Studio Code – VS Code extension
  • Eclipse – Eclipse IDE plugin

System Integrations​

  • Git – commit and branch analytics
  • GitHub/GitLab – pull requests, merge requests, and issue context
  • CI/CD – build and test pipelines
  • Jira/Trello – synchronisation with work items and roadmaps

Collection Agents​

  • Local agents – run on developer machines
  • Server agents – aggregate data on dedicated infrastructure
  • Cloud agents – operate inside hosted environments

Security and Privacy​

Data Anonymisation​

  • Personally identifiable information is stripped or anonymised
  • Metrics are linked to pseudonymous identifiers
  • Individual developers cannot be reverse engineered

Encryption​

  • TLS/SSL protects data in transit
  • Local caches are encrypted at rest
  • Access is granted only through authenticated APIs

Access Control​

  • Role-based permissions govern who can view specific datasets
  • Every operation is captured in audit logs
  • Data deletion on request is supported

Storage Options​

Local Storage​

  • Agents cache data locally on workstations
  • Periodic synchronisation pushes updates to the server
  • Offline mode keeps working even without connectivity

Cloud Storage​

  • Secure backups in the cloud
  • Automated backup and disaster recovery
  • High availability and elasticity

Data Processing​

Aggregation​

  • Metrics are grouped by team, project, and timeframe
  • Time-series windows power trend analysis
  • Filtering and grouping expose the signal that matters

Analysis​

  • Statistical models identify patterns in the data
  • Machine learning highlights anomalous behaviour
  • Outlier detection flags unusual trends worth investigating

Visualisation​

  • Dashboards surface the KPIs that matter most
  • Charts and diagrams reveal trends over time
  • Data export enables further processing in external tools