Developer Productivity Engineering Blog

Although ephemeral builds can be nice to test changes in isolation, there are trade-offs for doing so. One of those trade-offs is dependency download times.

In Develocity 2019.3, the dependency download metrics in the performance and trends dashboards can be used to look at dependency download times and debug issues. For example, let’s look at the dependency download times for the Gradle project itself for all builds run in the last four weeks.

In this webinar, Hans Dockter, CEO & Founder of Gradle, talked about the emerging practice of developer productivity engineering, a discipline of using data to improve essential development processes from build/test to CI/CD.

Changing one dependency declaration can have unexpected side effects through its transitive dependencies. For an example of tightly-coupled dependencies, let’s look at a simple Spring Boot project with the following build.gradle file.

Have you ever had an infrastructure failure break your build, or perhaps been forced to stop working in order to fix broken build configuration? Changes to development infrastructure often happen unbeknownst to the engineers it serves. Many problems go unreported, but chip away at our productivity much more than we realize. It is important to identify and stop non-verification failures (those not caused by routine development, such as compile and test failures) before they disrupt your organization. To combat this, we are pleased to introduce new failure analysis tools.

Whether you use a mono-repository or multi-repository approach for your sources, managing dependencies is central to productivity by reusing artifacts and ensuring a quality build. A single incorrect dependency can break existing code, introduce a vulnerability or simply prevent your project from building.

Android builds can be complex and challenging to optimize, yet Android teams usually have the fewest resources to work on the build. In this webinar, Jason Schroeder from Salesforce and Rooz Mohazzabi from Gradle discuss top Android build issues an how to solve them.

We found out that compilation speed is not affected much by instance size, while the price doubles with every step. The most cost-effective instance is thus m5.large (2 cores). But what happens when we throw in our Hydra parallel Scala compiler?

Do you ever watch your IDE sync or build run and think to yourself: “This is slow. I think this used to be much faster”? I do, more often than I’d like to admit.

Build logic can grow as complex as application logic and because of that good tooling is key for a productive build-logic authoring and maintenance experience. In this webinar, Paul Merlin and Rodrigo B. de Oliveira from Gradle explain how the Gradle Kotlin DSL leverages the Kotlin language and its great tooling to provide just that.

Whether you use a mono-repository or multi-repository approach for your sources, managing dependencies is central to productivity by reusing artifacts and ensuring a quality build.