software-engineering-metrics.md

Software Engineering Metrics

• See
  • [productivity]
  • [leadership] -> OKR's

#metrics

• Engineering in a Hybrid World The data behind high-functioning engineering organizations

Common Metrics

• Writing Code

  • Time spent on planning / requirements gathering
  • Time from requirements to code complete
  • % of code delivered vs. committed
  • num of story points / features written

• Code Review

  • Time spent on code review
  • Time from review request to merge

• Testing

  • % code coverage
  • num test cases
  • % of code passed
  • Build / testing time
  • num critical defects

• Deployment

  • num updates / releases
  • PR to Release time
  • num of rollbacks
  • % of roadmap/committed
  • development work shipped on time

• Maintenance

  • num incidents / outages
  • Cost of poor quality
  • Service uptime
  • num of SLA breaches

• Metrics

  • Book Accelerate: Building and Scaling High Performing Technology Organizations

    • measure software delivery performance - and what drives it - using rigorous statistical methods

  • Applying Advanced Agile Metrics (AAAM) course
  • DevOps Metrics Platform - Measure and improve your organization’s DevOps performance.
    • From plugins in developers IDE's to measuring the time for tickets testing, merging and release
  • Metrics-driven product development is hard
  • Be good-argument-driven, not data-driven

    • Data has its place. Metrics are a useful tool for making a certain class of persuasive arguments in certain domains. But they are only a tool for making good arguments. Data is not an end in itself. A weak argument founded on poorly-interpreted data is not better than a well-reasoned argument founded on observation and theory. Stop going all googly-eyed (tee hee) at statistics. Metrics are tempting. They promise easy answers. Resist! Be skeptical! Have no tolerance for poor arguments made with data. Keep intrinsic motivation alive.