Systems | Development | Analytics | API | Testing

Between the relentless pressure to ship new features and updates, and the increasing adoption rates of DevOps, AI’s influence in the future of the testing landscape is palpable, and the focus in the coming years will be applying that to QA teams everywhere.

There are few things that can derail developer productivity and undermine your pipeline like a flaky test. Testing is the backbone of a good development process, ensuring that your code is as accurate and usable as possible. When these tests point towards faulty development, the impacts can be significant. This information is predicated on an assumption, however – the assumption that what the test says is accurate.

Have you ever found it a bit tedious to enter the same release notes text every time you submit an app for review in the stores? In this article, I’ll introduce a method to automate this small but repetitive task. It’s very easy to implement, so please read on.

When I first started writing Go software a little over a decade ago, one of the features I found particularly intriguing was the ability to build statically-linked binaries for multiple operating systems and architectures without a lot of headache. This build toolchain feature is widely relied upon by nearly all Go developers, especially when needing to build multi-arch container images destined to be run in a Kubernetes cluster consisting of amd64 and/or arm64 nodes.

When evaluating a DevOps testing service, software teams should ask the following questions — it can mean the difference between quickly shipping high-quality code and bottlenecking releases, or having bugs slip into production: In this article, we review five top DevOps testing services, starting with our own, Rainforest QA. We show how we help teams ship high-quality code without constant bottlenecks dragging down the release pipeline.

Generative AI is quickly becoming ubiquitous in the software development space, with tools like Anthropic’s Claude offering rapid methodologies for code iteration, testing, and deployment. As new solutions, such as MCP (Model Context Protocol), are created to make integration more seamless, enterprises are adopting these AI solutions to optimize their development processes, a familiar challenge repeatedly arises: cost.