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

_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. What’s not to love?

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.

Transcript

1. Enterprise Developer Productivity Engineering What’s in it for me?
2. ⬢ Lead Developer Advocate ⬢ Java Champion ⬢ 20+ years Java experience ⬢ …and author Trisha Gee
3. https://trishagee.com/books/
6. 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
7. “Bottlenecks in the toolchain are holding back the rockstar 10x developers” Pete Smoot, Software Architect, Dell Technologies
10. The “best” programmers outperformed the worst by roughly a 10:1 ratio
11. What Mattered?
12. ⬢ Paired programmers performed at roughly the same level What Mattered?
13. ⬢ Paired programmers performed at roughly the same level ⬢ The average difference was only 21% between paired participants What Mattered?
14. ⬢ 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 What Mattered?
15. ⬢ 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?
16. “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
17. “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
18. Though the phrase had not yet been coined, increased productivity came down to developer experience.
19. Gradle is Pioneering DPE DPE is a new software development practice used by leading software development organizations to maximize developer productivity and happiness.
20. What Problems Does DPE Solve?
22. 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
23. A 10x organization should be reducing build and test feedback times and improving the consistency and reliability of builds
24. Pain Point: Waiting for Builds & Tests to Complete
25. Are you tracking local build and test times?
28. The only initiatives that will positively impact performance are ones which increase throughput while simultaneously decreasing cost
29. 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
30. Very Fast Feedback Is Important
31. Solution: Acceleration Technologies
32. Build Caching Speeds up Builds and Tests
33. ⬢ 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
34. Build Caching When the inputs have not changed, the outputs can be reused from a previous run.
35. Demo: Build Cache for Maven and Gradle
36. Remote Build Cache ⬢ Shared among different machines ⬢ Speeds up development for the whole team ⬢ Reuses build results among CI agents/jobs and individual developers
37. Test Distribution Parallelizes Test Execution
38. 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
39. 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' 
 }
 } 
 }
40. 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
41. Test Distribution in Gradle Enterprise
42. Test Distribution Results ‑ ~50% ‑ ~50% ‑ ~50% Measurements from the demo project Doubling the number of executors cuts build time in half
43. Netflix reduced a 62-minute test cycle time down to just under 5 minutes!
44. Machine learning leads to greater efficiencies
45. 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
46. 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.
47. Is the build and test cycle fast enough?
48. Is the build and test cycle fast enough?
49. Is the build and test cycle as fast as it can possibly be?
50. Pain Point: Inefficient troubleshooting of broken builds
51. “ You can observe a lot by just watching.” Yogi Berra, Catcher and Philosopher Blank background use at will
52. Build Scan: scans.gradle.com
53. Learn more https://bit.ly/grdl-scan
54. DPE Organizations Track Failure Rates
55. Pain Point: Flaky Tests & Other Avoidable Failures
56. Flaky builds and tests are maddening
57. ⬢ 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?
58. Identify and Track Flaky Tests
59. https://youtu.be/vHBzZHE4tJ0
60. Pain Point: No Metric/KPI Observability
61. Without focus, problems can sneak back in
62. 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.
63. “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
64. Pain Point: Inefficient use of CI Resources
65. All Of This Will Improve CI Body text
66. In Summary
67. ⬢ 10x Developers might be a myth, but 10x Organisations are real In Summary
68. ⬢ 10x Developers might be a myth, but 10x Organisations are real ⬢ Developer Productivity is deeply linked to Developer Experience In Summary
69. ⬢ 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 In Summary
70. ⬢ 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 In Summary
71. ⬢ 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 In Summary
72. ⬢ 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
73. Source: TechValidate. TVID: 066-EEE-DB1
74. DPE Transforms Every Business Layer
75. Next Steps
76. https://bit.ly/speed-build Build speed challenge
77. There’s a Book for This
79. https://bit.ly/dpe-4me
80. Thank you!
81. How it works… 1. When a test run starts, the build tool submits a test input snapshot and test set to a machine learning model. 2. PTS automatically develops a test selection strategy by learning from historical code changes and test outcomes from your Build Scan data to predict a subset of relevant tests, which are then executed by your build. 3. Code change and test results data are processed immediately after a Build Scan is uploaded to PTS and updates the test selection strategy based on new results.
82. Cache Key/Value Calculation The cacheKey for Gradle Tasks/Maven Goals is based on the Inputs: cacheKey(javaCompile) = hash(sourceFiles, jdk version, classpath, compiler args) The cacheEntry contains the output: cacheEntry[cacheKey(javaCompile)] = fileTree(classFiles) For more information, see: https://docs.gradle.org/current/userguide/build_cache.html