Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Developer Productivity Engineering: What's in it for me? (DevBcn)

Developer Productivity Engineering: What's in it for me? (DevBcn)

It may surprise you to learn that we developers are a patient, tolerant species. People pay us to do what we enjoy - write code and create working applications. In return, we will put up with all sorts of blockages and toil that get in the way of this - long build times, flaky tests, hard-to-debug toolchain failures and so on.

Is this truly the price we need to pay? Could there be a better world, where the build is as fast as it could possibly be? A world where problems that affect many developers are quickly identified and fixed?

Welcome to the world of Developer Productivity Engineering, where we can get computers to do what they’re good at (automation) to make developers’ lives easier, and make us more effective at our jobs. And while developer joy may be a difficult thing to sell to decision-makers, effective developers who are making the best use of their time, and their hardware, have a direct impact on an organization’s ROI.

In this talk, Trisha will explore what DPE is, give you some practical ways to get started, and discuss ways to help the leaders in your organisation to understand the enormous value DPE could unlock.

Trisha Gee

July 03, 2023

More Decks by Trisha Gee

Other Decks in Technology


  1. ⬢ Lead Developer Advocate ⬢ Java Champion ⬢ 20+ years

    Java experience ⬢ …and author Trisha Gee
  2. But Bottlenecks to Productivity are Everywhere Code Code Wait Time

    for Local Build Debug Build Failure Lunch Code Wait Time for Local Build Investigate/Fix Flaky Tests Sprint Waiting time for CI Build
  3. How developers spend their time Source: The 2019 Tidelift managed

    open source survey results https://bit.ly/3MOEpK3
  4. “Bottlenecks in the toolchain are holding back the rockstar 10x

    developers” Pete Smoot, Software Architect, Dell Technologies
  5. ⬢ Paired programmers performed at roughly the same level ⬢

    The average difference was only 21% between paired participants ⬢ They didn’t work together on the task, but they came from the same organization ⬢ The best organization performed 11.1x better than the worst What Mattered?
  6. “While this productivity differential among programmers is understandable, there is

    also a 10 to 1 difference in productivity among software organizations.” Software Productivity in the Enterprise Harlan (HD) Mills https://trace.tennessee.edu/cgi/viewcontent.cgi?article=1010&context=utk_harlan
  7. “The bald fact is that many companies provide developers with

    a workplace that is so crowded, noisy, and interruptive as to fill their days with frustration. That alone could explain reduced efficiency as well as a tendency for good people to migrate elsewhere.” Peopleware: Productive Projects and Teams, Third Edition Tom DeMarco, Tim Lister
  8. Gradle is Pioneering DPE DPE is a new software development

    practice used by leading software development organizations to maximize developer productivity and happiness.
  9. DevOps, 12-Factor, Agile, etc, have still not captured all bottlenecks,

    friction, and obstacles to throughput Many are hiding in plain sight, in the developer experience itself
  10. A 10x organization should be reducing build and test feedback

    times and improving the consistency and reliability of builds
  11. The only initiatives that will positively impact performance are ones

    which increase throughput while simultaneously decreasing cost
  12. Faster Builds Improve Creative Flow Team 1 Team 2 No.

    of Devs 11 6 Build Time 4 mins 1 mins No. of local builds 850 1010
  13. ⬢ Introduced to the Java world by Gradle in 2017

    ⬢ Used by leading technology companies like Google and Facebook ⬢ Can support both user local and remote caching for distributed teams Build Caching
  14. Remote Build Cache ⬢ Shared among different machines ⬢ Speeds

    up development for the whole team ⬢ Reuses build results among CI agents/jobs and individual developers
  15. Existing solutions: Single machine parallelism Parallelism in Gradle is controlled

    by these flags: -- parallel / org.gradle.parallel 
 Controls project parallelism, defaults to false -- max-workers / org.gradle.workers.max 
 Controls the maximum number of workers, defaults to the number of processors/cores test.maxParallelForks 
 Controls how many VMs are forked by an individual test task, defaults to 1 See https://guides.gradle.org/performance/#parallel_execution for more information
  16. Existing solutions: CI fanout See https://builds.gradle.org/project/Gradle for an example of

    this strategy Test execution is distributed by manually partitioning the test set and then running partitions in parallel on several CI nodes. pipeline {
 stage('compile') { ... }
 parallelStage('test') {
 step {
 sh './gradlew :testGroup1' 
 step {
 sh './gradlew :testGroup2' 
 step {
 sh './gradlew :testGroup3' 
  17. Assessment of existing solutions ⬢ Build Caching is great in

    many cases but doesn’t help when test inputs have changed. ⬢ Single machine parallelism is limited by that machine’s resources. ⬢ CI fanout does not help during local development, requires manual setup and test partitioning, and result collection/aggregation
  18. Test Distribution Results ‑ ~50% ‑ ~50% ‑ ~50% Measurements

    from the demo project Doubling the number of executors cuts build time in half
  19. Predictive Test Selection 01 Instead of trying to analyze which

    tests could possibly be impacted by developer changes, Predictive Test Selection looks at the history of changes and what has happened to tests in the past 02 When tests complete, they can either FAIL, SUCCEED, or be FLAKY. Predictive Test Selection will predict the outcome of the test based on the history it is analyzing 03 PTS will recommend skipping tests that are successful, and will only run tests that are likely to provide valuable feedback https://arxiv.org/pdf/1810.05286.pdf
  20. Force multiplier when used in combination 1. Build Cache. Avoid

    unnecessarily running components of builds and tests whose inputs have not changed. 2. Predictive Test Selection. Run only the relevant subset of test tasks likely to provide useful feedback. 3. Test Distribution. Speed up the execution of the necessary and relevant remaining tests by running them in parallel. 4. Performance Continuity. Sustain Test Distribution and other performance improvements over time with data analytic and performance profiling capabilities.
  21. “ You can observe a lot by just watching.” Yogi

    Berra, Catcher and Philosopher Blank background use at will
  22. ⬢ Try it again ⬢ Re-run it ⬢ Re-run it

    again ⬢ Ignore it and approve PR ⬢ All of the above The test is flaky. What do you do now?
  23. “…our analysis revealed that re-running the failing build and attempting

    to repair the flaky test were the most common actions.” Surveying the Developer Experience of Flaky Tests https://mcminn.io/publications/c72.pdf
  24. “…our analysis revealed that re-running the failing build and attempting

    to repair the flaky test were the most common actions. Our findings also suggested that developers who experience flaky tests more often are more likely to take no action in response to them.” Surveying the Developer Experience of Flaky Tests https://mcminn.io/publications/c72.pdf
  25. Continuous Improvement: It doesn’t really matter what you improve as

    long as you are constantly improving something, because… …entropy denotes that if you aren’t doing anything, you’re always getting worse.
  26. “The tools, services, and environments that developers need to do

    their jobs should be treated with production-level SLAs. The development platform is the production environment for the job of creating software” Release It! Second Edition Michael Nygard
  27. ⬢ 10x Developers might be a myth, but 10x Organisations

    are real ⬢ Developer Productivity is deeply linked to Developer Experience ⬢ If you do nothing about productivity, life will get worse ⬢ Fast feedback, efficient troubleshooting, and reliable cycles are key ⬢ Start with observation, and then take action on data ⬢ Proactively solve problems for the whole team In Summary