For a software engineer, even the basic use of a debugger can save a lot of pain: adding breakpoints (places in the code the program will stop at and expose the current context) is very easy, and navigating from one breakpoint to another isn't difficult either. And with just that, you can say goodbye to a program's many puts and runs. Just add one or more breakpoints and run your program.

In this latest entry of our fascinating interview series that focuses on major players in the global tech arena, we are delighted to present Miguel Jetté, Vice President of Artificial Intelligence at Rev. Join us as we dive into his career development, provide tips for aspiring AI leaders, and discuss the key lessons he has learned along the way.

API-database coupling vs. traditional multi-layered architectures: what’s the difference and why does it matter? The main difference between direct API-database coupling and multi-layered architectures is that the former allows the API to interact directly with the database, minimizing latency and complexity, while the latter uses multiple layers to separate concerns.

HubSpot, Snowflake and Fivetran marketing executives discuss how a modern data stack enables enterprises to meet customer expectations and drive results.

We did a sort of time travel on 20th April at QonfX. If you are not one of the 3000+ people who registered for this event, it is a unique software testing conference that keeps its focus on the Future of Testing. This year was the second edition of QonfX and received even more love than the last time. Feedback like the above filled our social feeds during and post QonfX. We cannot keep a count of the number of times attendees used the words ‘eye-opening’ for the talks given by the speakers.

Kernighan & Pike, The Practice of Programming, 1999 Despite constantly changing technologies and the needs of customers, some wisdom seems eternal. Programmers need to test their code. But thorough testing takes time. When we do it well, everything works, and a massive testing effort feels like a waste. However, when we do it badly, our code is often broken, and we wish that we had done better testing. I have some good news for you.

At the end of every testing project, a test report is usually created to summarize the results. This report provides insights into how the test project was executed if it aligned with the initial plan, and what areas need further optimization. In this article, we’ll explore in-depth what needs to be included in a test report, as well as the key metrics that QA teams need to look at if they want to gauge their testing efficiency.

Generative AI (GenAI) has taken the world by storm, promising to revolutionize industries and transform the way businesses operate. From generating creative content to automating complex tasks, the potential applications of GenAI are vast and exciting. However, implementing GenAI in an enterprise setting comes with its own set of challenges. At Cloudera, we understand the complexities of enterprise GenAI adoption.

Any (functioning) system is built upon an infrastructure. This infrastructure supports all of the functions and activities of the system. Testing is no exception. With a good test infrastructure, QA teams can speed up their testing efforts and drastically improve efficiency. In this article, we will explore the concept of test infrastructure, and how you can effectively build one.

For enterprises, commercial use of AI is still in its early stages, and it’s a case of risk and reward, weighing up both and investigating the best way forward. Of course, there’s much to gain from the use of AI. Already, companies are providing better customer service, parsing complex information through natural language inputs, and generally making workflows faster.