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QonfX 2024 Rewind: Testing, AI, and the Future

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.

How ClearML Helps Teams Get More out of Slurm

It is a fairly recent trend for companies to amass GPU firepower to build their own AI computing infrastructure and support the growing number of compute requests. Many recent AI tools now enable data scientists to work on data, run experiments, and train models seamlessly with the ability to submit their jobs and monitor their progress. However, for many organizations with mature supercomputing capabilities, Slurm has been the scheduling tool of choice for managing computing clusters.

Debugging in Ruby with pry-byebug

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.

Direct API-Database Coupling vs. Multi-Layered Architectures

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.

GenAI: Navigating the Risks That Come with Change

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.

What is a Test Report? A Comprehensive Guide To Build One

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.

Effective Testing in JavaScript

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.

The Real-World Impact of Shift-Left

To shift or not to shift – that is the question. If you’ve been around the software development world lately, you’ve likely heard of shift-left – the practice of integrating testing, security, and operations early in the software development lifecycle to detect issues early. This approach is meant to be a win-win-win – saving time, money, and headaches. But is it as great in practice as it sounds in theory? In this episode, we debate the real-world benefits of shift-left and get to the heart of the issue: testing well.