3 key themes to take away from the first-ever DPE Summit

Last month, Gradle hosted the first-ever Developer Productivity Engineering Summit for two days in San Francisco. The event, which focused on the emerging practice of DPE, featured an all-star roster of speakers from many of the most recognizable technology and business brands, including Airbnb, Amazon, American Express, Apple, Google, LinkedIn, Meta, Netflix, and Uber. Key themes that emerged from the Summit were shared in a recent article published by Spiceworks. They included:

• From Best Practices to Next Practices
• Leveraging Modern Technologies to Transform the Developer Experience
• Mainstreaming DPE: Journeys, Success Stories, and Lessons Learned

The neuroscience behind developer productivity engineering

At last month’s DPE Summit, Hans Docker delivered a keynote that discussed the neuroscience behind developer productivity. It provided a thought-provoking take on the psychological root causes and “cognitive fatigue” associated with developer bottlenecks like unnecessarily long feedback cycles, protracted troubleshooting times, and toolchain failures.

Hans described how a lack of understanding of this dynamic leads to a misalignment between leadership’s incremental perspective on the potential impact of DPE and that of their developers, who feel the cognitive pains every day and see the impact of addressing these pains as transformational.

Translating the productivity benefits of removing cognitive fatigue pain into a hard business case for leadership consumption remains a challenge. But, meeting that challenge is the key to realizing the “DPE multiplier,” which is the number of times developers say they could be more effective with a more efficient toolchain (usually 2-5x). The presentation concludes by connecting productivity, developer experience, and joy.

In addition to Hans’ keynote:

• Adam Rogel from DoorDash delivered his keynote titled, “Why DPE is Needed Now More Than Ever,” exploring the importance of prioritizing developer empathy
• Michael Bailey from American Express discussed how many small productivity improvements add up and the value of focusing on personal developer productivity in a presentation titled "Communicating for Productivity"

Dogfooding test distribution for maximum effect

Test Distribution speeds up your builds by distributing your test cases across multiple agents. Using data from your build history determines how long each test is likely to take and distributes the tests intelligently. It works for local, and CI builds. When the build is finished, all the results from every agent’s tests are conveniently available in a single Gradle Enterprise Build Scan™.

It’s easy to distribute most unit tests, but some tests have special requirements that make it more difficult. In this blog, the team at Gradle described their approach to three kinds of tests that are much more difficult to distribute:

• Tests that require a database
• Tests that use browser frameworks such as Selenium or Cypress
• Tests that require virtualization platforms such as Docker or Vagrant

How Big Data and AI can help the developer toolchain

Datanami Managing Editor Alex Woodie recently interviewed Hans Dockter about how big data and AI supports the practice of Developer Productivity Engineering and the big role it will play in advancing the discipline in the future.

Woodie writes, “The company (Gradle) has established a data science team and rolled out the first AI-based product. Predictive Test Selection uses machine learning to predict which parts of the codebase are sensitive to change and which tests can be safely excluded from the DevOps lifecycle.

‘We can tell you, oh, you changed that part of the software. 9,000 of the 10,000 tests you don’t need to run, because we know from our data that those tests are completely insensitive to those areas of the code,’ Dockter says. ‘Only 1,000 tests are sensitive to this area, so let’s only run 1,000 out of 10,000 tests.’

Advanced analytics and AI are critical to making sense of observability data, Dockter says, and Gradle will have more AI and analytics products to help customers soon. “We think at scale, only with advanced analytics and machine learning, can you really get the full benefits from that data for your developers.”

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