Much like the book, Thinking, Fast and Slow, testing often falls into one of two buckets, ‘fast’ and ‘slow’. Fast, unconscious-like testing consists of quick checks, with little or no thinking to determine if a test should pass or fail. Fast tests click through test case steps with haste and obviously complete quickly but only focus on what is necessary to complete the test. Slow testing is where the computer, or human, takes a bit more time to measure, compare, or consider how to execute the test case, and to decide if a test should be marked pass or fail. It is critical to know when to test fast and when to test slow.

How do the most well-funded, sophisticated software test teams on the planet test everything? — they don’t! There is a new way to test that delivers great coverage, finds important bugs early, and ensures that core functionality works on every new release.

Join test.ai’s chief scientist Tariq King at the Quest for Quality Conference Online as he goes beyond functional test automation with AI in his presentation Beyond Functional Automation With AI. Bots are no longer just doing functional testing, they are testing usability, accessibility, trustworthiness, video streaming, and gameplay. Tariq will walk through how AI is being used to tackle these difficult, real-world testing problems commonly thought of as being impossible to automate.

Test.ai’s own Chris Navrides is presenting at STARWEST Virtual on Thursday. Come find out how you can write tests like a test plan, and let the AI and machine learning do the hard work. See how tests can be written for any app, and run across a variety of platforms and form factors. Using the AI allows you to also get detailed system and performance information that needed to make quality evaluations.

Chris’ presentation, Testing Apps with AI, will be on October 8, at 2:15pm ET.

Since the world is filled with bias, it follows that any data we collect from it contains biases. If we then take that data and use it to train AI, the machines will reflect those biases. So how then do we start to engineer AI-based systems that are fair and inclusive? Is it even practical to remove bias from AI-based systems, or is it too daunting of a task? In this article, we explore the world of AI bias and take a look at it through the eyes of someone tasked with testing the system. More specifically, we describe a set of techniques and tools for preventing and detecting unwanted bias in AI-based systems and quantifying the risk associated with it.

The beginning of every great transformation in technology brings out these same marketing patterns. Feel free to indulge in the chaos of it all, as I do, but, be careful of which AI you bet on.

A Q&A session with test.ai CEO Jason Arbon.

A testing/agile company asked the following questions but didn’t publish the QnA — perhaps because my take on ‘agile’ wasn’t in-line with their overall message? Maybe it wasn’t ‘vendor-friendly’ enough? Most likely they just forgot or didn’t care enough about the answers to bother publishing it. I prefer to imagine a conspiracy :)

It is time to get serious about the field of software testing. In the last decade, software started to power our day-to-day lives, infrastructure, and the economy. The software testing community fragmented as it grew. Under-resourced testing teams, scattered around the world in isolated cubicles, started each new project from scratch, and worked on increasingly complex and diverse software. Software is far more useful and amazing than ever in 2020, but people encounter software failures nearly every day. It is time to get serious about software testing in the 2020’s. If you are reading this — the world needs you.

Test.ai chief scientist Tariq King rounded up the PNSQC Speakers of the House for 10 minutes on the hottest issues, problems, and solutions in the world of software engineering. Every attendee at PNSQC19 was invited to submit their proposal to Tariq through the mobile app, letting the lightening round be built in real time.

Missed the event? The lightening talks are available to watch right now!

Software testers should welcome this new world of AI-driven testing. Most testers are worried their jobs will be replaced, or the AI will have all the fun. This transition to AI-driven testing is an opportunity for testers if they seize the moment. Testers can easily make their jobs more interesting and more profitable. What does this new world of testing look like?

Test.ai is excited to introduce OpenTestData.org, an open-source repository of test data for teams to speed up testing.

Tl;dr: We’re still test.ai. We’re just updating our logo, so keep calm and test on.

AI can beat humans in the games of Chess, Jeopardy, and Go. Recently, I’ve also discovered that AI systems can also beat 70 human testers at their own game — testing. I spend my days coercing machines to execute test cases with techniques such as neural networks, clustering, and reinforcement learning. It is a lot of work, but it is somewhat mechanical and obvious that with AI driving cars around town and playing Atari video games these days, the AI should also be able to execute a test case or two for us.