In software testing, smoke tests are a small set of end-to-end tests that make sure the most essential functions of an application or website work properly. Many software teams run a smoke testing suite of 10 to 15 tests as a preliminary step before running an entire regression suite (which could have anywhere from 50 to 500+ tests) to make sure the app is stable enough to merit further testing.
No code and low code test automation are becoming widely adopted and will help address the evolving challenges faced by businesses today. However, what is the difference between no code and low code? When is it useful for an organization to adopt? How useful are their capabilities? Will this dampen the need for skilled Automation Engineers, Software Development Engineers in Test (SDET), or traditional Quality Assurance (QA) Specialists?
Keeping your dependencies up-to-date is essential to ensure that your applications stay healthy, secure, and performant. Thankfully, the BEAM ecosystem has its own package manager, Hex, which is fast, mature, and simple to use. This article explores the available tools and commands to manage Hex dependencies and some tips to make the process more enjoyable. Let's dive in!
How often do you face an issue with a service that doesn't work and you don’t know why? How often is it related to some external services, dependencies like a database, or queue mechanism you are using? Of course, you could check each service from time to time, and you could run some smoke tests against the service, but it will not give you an answer if “service is not working because the connection to MSSQL failed.”
If you’re a data leader at an early-stage or high-growth company, you’re in a unique position to promote data appreciation. Don’t waste your golden window of opportunity. Take this moment to institute best practices, promote good habits, and build the foundation for a data-driven culture. There’s no universal data strategy that would work for all organizations — wouldn’t that be great? — but there are pitfalls that all organizations should watch out for.
Three decades into the data revolution, my fellow technologists and I find ourselves asking an existential — and rather distressing — question: What happened to the promise of customer 360? The ability to get more customer data was supposed to fundamentally change the relationship between customers and brands. Companies were going to be able to offer targeted, meaningful engagements that would multiply average deal size and slash time to close.