what we learned from community metrics agenda
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What we learned from Community Metrics Agenda Why are metrics - PowerPoint PPT Presentation

What we learned from Community Metrics Agenda Why are metrics used? How metrics are used in two Open Source communities Common pitfalls and ways to avoid them Why use Metrics? Transparency Who is contributing to the


  1. What we learned from Community Metrics

  2. Agenda • Why are metrics used? • How metrics are used in two Open Source communities • Common pitfalls and ways to avoid them

  3. Why use Metrics? • Transparency • Who is contributing to the community? • Where (both organizationally & geographically) are the contributions coming from? • Help identify potential bottlenecks • Are code reviews being done in a reasonable timeframe? • Are bugs being closed? • Are new community members able to quickly participate? • Encourage community participation

  4. How Metrics are used in OpenStack • Yearly and per-cycle reviews • Basic metrics • Trend analysis • Published activity metrics • Extracting data from tools • Target for gaming

  5. How Metrics are used in OPNFV Quarterly review of: ● Review Efficiency ● Time to Merge ● Backlog Management

  6. Pitfalls and unintended consequences... • “We’re one of the top X contributing organization in Project ABC!” • “Project XYZ is one of the fastest growing projects based on …” • People trying to game their contribution statistics • e.g. submitting multiple patches for simple changes (in order to maximize patch counts) • Too much focus on code contributions

  7. Pitfalls and unintended consequences... • Making comparisons between different communities based on a few metrics • Or even making historical comparisons for the same community • Ignoring non-metrics “Those who believe that what you cannot quantify does not exist also believe that what you can quantify does” --Aaron Haspel

  8. Some of the flaws of metrics • People often measure the most easily measurable • Focusing on input vs. outcomes • Less insight through standardization • Ignores intrinsic motivation

  9. Recommendations for metrics in open source communities • Metrics should NOT be used as a basis to reward people • Lot of research questioning pay-for-performance as this has an effect of reducing intrinsic motivation • Consideration for using metrics for internal monitoring vs. external purposes (e.g. reward/punishment) • Using metrics as a basis for reward will likely increase the likelihood of “gaming” • Metrics are better used for identifying outliers (e.g. poor performers)

  10. Recommendations for metrics in open source communities • Should be developed from the bottom up with community input • There shouldn’t be metrics people vs. the rest of the community • Metrics should lead to informed interpretations and judgement • Should be conducted by people who are familiar with the environment/community and can compare to previous conditions • Hard part is knowing what metrics are meaningful and what they mean • “Measurement demands judgement”

  11. Parting questions/thoughts • What metrics make sense for our community? • Are we looking at everything that matters? • What are the numbers telling us and how should they be presented? • What about things that can not be counted? • Some factors are beyond your control (incl. metrics)

  12. “Extrinsic rewards become an important determinant of job satisfaction only among workers for whom intrinsic rewards are relatively unavailable” ➢ Barry Gruenberg: “The Happy Worker: An Analysis of Educational and Occupational Differences in Determinants of Job Satisfaction,“ American Journal of Sociology 86 (1980), pp247-71

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