Systems | Development | Analytics | API | Testing

Imagine you open a shopping website, browse around and see some products of your liking. After placing them in the cart, you're all ready to check out, only to discover that your favorite items are nowhere to be found on the list. This is one of many examples of a software bug that could be caught using integration testing. There are multiple testing stages that software goes through before release, the two most popular being unit and integration testing.

Git LFS — also known as Git Large File Storage — is used by many teams to manage and store large files. Here, we break down what Git LFS is, what it does, when to use it, and more.

Software testing is the process of checking the quality of a product before launching. It measures the software's completeness to the single functional (or non-functional) attributes and whether it has fulfilled the business logic or shown us the missing gaps in requirements that need immediate tackles. Software testing is an integral part of the software development life cycle. Over time, software testing activities have evolved to meet the ever-increasing complexity of applications.

Every software engineer dreams of the day when they can simply type English-language sentences into a development platform and out pops a new, production-ready app. The app will be free of bugs because the natural language processing (NLP) functionality of the platform will have automatically created the test cases, run the tests, and self-corrected for changes to the app features. AI researchers in software testing are heavily invested in making testing as autonomous and natural as possible.

Midnight, December 31, 1999, marked the beginning of a millennium where aircrafts would not fall out of the sky and bank ATMs would not spew millions of dollars from their cash withdrawal slots. Preemptive software testing and remediation on a global scale provided the automated world with some assurance that the machines would continue to run as they had for decades.

A mock server can prove useful in many circumstances. Imagine you’re an engineer working on optimizing a feature inside of an existing API that relies on multiple other microservices to function properly. To fully test the optimizations, you’ll have to set up test versions of all the dependencies, which quickly proves to be quite a task in and of itself. This is where a mocks—a server that simulates the behavior of a real server—can be very beneficial.

We love our mobile phones. We take pictures, text friends, and sometimes even use them to make a phone call. For many of us, a mobile phone is about the apps. Apps help us stay connected, inform us, and make our work easier. Yet according to one study, users uninstall 49% of apps within the first 30 days of installation. Why? There are many reasons, including the app being too spammy, taking up too much storage space, or the user just didn’t need it anymore.

The idea behind this blog post originated when we updated our documentation after the release of our new UI. With more than 250 blog posts at the time I’m writing this one, you can see how this could prove challenging. And of course the twist is that we also took this opportunity to reorganize the documentation so it’s not as simple as a search and replace of the domain..