Observability in Software Engineering

• Plan:
  • Teach programmers to distinguish reliable research from plausible punditry
  • Use the problem of programmer productivity as the motivating issue
• Pro:
  • Topical: debates about the impact of AI are vacuous without an understanding of what productivity means
  • Motivates study of both qualitative and quantitative methods
  • Practical exercises are straightforward to create
• Con:
  • Medium effort to create
  • May be seen as niche: people won't understand observability and productivity are just being used as a working example

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Learner Persona

• Dav, 32, has been working in mobile application development for ten years. Originally a front-end developer, they now split their time between managing a team of three and building out test infrastructure for that team and others.
• The last two projects that Dav worked on shipped late despite late hours from their team, and had an embarrassing number of bugs. As a result, senior management wants more insight into progress and productivity, but Dav is worried that simple metrics like "issues closed per week" will be both misleading and abused.
• Dav wants to learn more about what makes programmers and programming teams productive (or unproductive) in general, and how to identify problems and bottlenecks without intrusive surveillance.
• Another release cycle is just about to start, so Dav's time is limited: they are able to take a day for a workshop, but have learned the hard way that they are unlikely to complete books or self-paced online courses.

Lessons

Appendices

Acknowledgments

• Greg Wilson is a programmer, author, and educator based in Toronto. He was the co-founder and first Executive Director of Software Carpentry and received ACM SIGSOFT's Influential Educator Award in 2020.

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