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Hi, I’m Tony, and I’m an engineer on League. This article is a followup to my performance series, where I talk about optimisation and profiling. This will be a high level overview of how we monitor game performance in League of Legends, how we detect when a performance degradation has slipped through QA and escaped into the wild, and how we track global trends in frame times over many patches and millions of players.

More than four years ago, Stash Bitbucket transitioned from running Javascript unit tests by Java to Karma. Today, I can announce that we switched our test runner to a modern tool called Jest! How did we end up here? Before Jest, our testing framework was a combination of different tools and helpers wired up together: Each of them serves a different purpose and is mandatory to write unit tests. The problem with that setup is that you need to maintain the custom integrations and this can cost you time and a lot of headaches.

In Continuous integration testing for the Linux kernel,I wrote about the Continuous Kernel Integration (CKI) project and its mission to change how kernel developers and maintainers work. This article is a deep dive into some of the more technical aspects of the project and how all the pieces fit together. Every exciting feature, improvement, and bug in the kernel starts with a change proposed by a developer. These changes appear on myriad mailing lists for different kernel repositories.

At QCon SF, Suudhan Rangarajan presented ‘Netflix Play API: Why We Built an Evolutionary Architecture’. Key takeaways from the talk included: services that have a single identity/responsibility are easier to maintain and upgrade; engineers should spend time identifying core decisions that need to be made when building a service, and determine whether these are ‘Type 1’ or ‘Type 2’ decisions which require thorough deliberation or rapid experimentation, respectively; and designing an ‘evolutionary architecture’, using tools like fitness functions, provides many benefits.

Test-driven development, or TDD, involves writing tests first then developing the minimal code needed to pass the tests. TDD is an established practice for feature development that can improve code quality and test coverage. What about other, non-functional requirements such as scalability, reliability, observability, and other architectural “-ilities”? How do we ensure operability and resiliency of features when they go to production? How can we encourage teams to build in these architectural standards, just as test-driven development builds in code quality and test coverage?