Conference Presentations

STAREAST The AI Testing Singularity
Slideshow

Most basic software testing will soon be done by a few individual, large systems. But today, software testing is a fragmented world of test creators, test automators, vendors, contractors, employees, and even “pizza Fridays” where developers roll up their sleeves and test the build themselves.
Jason Arbon
Agile DevOps East AI Is Key to Agile Testing Speed
Slideshow

Speed is king in agile. In a world where most of the agile process is automated, testing is the slowest and most expensive part of getting your app or website deployed to the world. Very few app teams have a decent amount of test automation, and even they still have days of manual testing during each agile cycle before they release new versions of their app. Testing is difficult, especially at the UI level, which is why it is still relegated to humans. But all that is changing with the application of artificial intelligence and machine learning. Join Jason Arbon as he explains how agile testing is ripe for disruption because AI itself is based on examples of input and output—which sounds a whole lot like the testing activity.
Jason Arbon
STARCANADA Behavior-Driven Testing Using Page Object Models
Slideshow

Does it feel like you spend half of every sprint fixing failing automated functional tests? Are programmers unwilling to work with automation code? Is test automation a maintenance nightmare? There is a better way. The Page Object Model (POM) is a powerful design pattern for building test...
Brian Hicks
STARCANADA No More Shelfware—Let's Just Drive Test Automation
Slideshow

When Isabel Evans learned to drive a car, she also learned how to check, clean, and change spark plugs, mend the fan belt with a stocking, and indicate speed and direction changes with arm and hand signals. Now, we don’t expect to have to do any of those things; we just drive the car...
Isabel Evans
STARCANADA Everything I Know about Automation I Learned from Saturday Morning Cartoons
Slideshow

Do you remember sitting in front of the television as a kid, enjoying your favorite Saturday morning cartoons? Chris Loder shows you how the lessons we learned from those cartoons apply to our everyday work in test automation. Wait until you hear what we’ve learned from the likes of Scooby...
Chris Loder
STARCANADA What’s Our Job When the Machines Do Testing?
Slideshow

After its highly hyped introduction decades ago and followed by a long, quiet “winter,” artificial intelligence (AI) has slowly crept back into our consciousness. While our Siri and Alexa assistants entertain us, machine learning (ML) has brought new conveniences into our lives...
Geoff Meyer
STARCANADA 7 Sure-fire Ways to Ruin Your Test Automation
Slideshow

Test automation projects fail, but why? Could you stop it from happening? In this tongue-in-cheek talk, Seretta Gamba will share seven proven methods to disrupt or utterly ruin a test automation project, including letting a lone champion keep important knowledge to themselves, ignoring good..
Seretta Gamba
STARWEST 2018 AI for Testing Tomorrow (Panel: Part II)
Slideshow

What does AI mean for the future of testing? What aspects of testing will the machines replace? What things will AI soon be better than humans at and what things will humans always do better than AI? This panel explores the future of AI for testing including thoughts on how humans can prepare for a future of testing where we work alongside AI. Hear experts discuss their views on the future impact of AI in testing and where the boundary between human and AI-powered testing truly lives.
Tariq King
Fighting Test Flakiness: A Disease that Artificial Intelligence Will Cure
Slideshow

Artificial Intelligence (AI) is making it possible for computers to diagnose some medical diseases more accurately than doctors. Such systems analyze millions of patient records, recognize underlying data patterns, and generalize them for diagnosing previously unseen patients. A key challenge is determining whether a patient's symptoms and history are attributed to a known disease or other factors. Software testers face a similar problem when triaging automation failures. They investigate questions like, Is the failure due to a defect, environmental issue, or nondeterministic test script? Is there current or historical evidence to support one belief over another? Join Tariq King as he describes how test failures and flakiness can be modeled for machine learning (ML) as causal disease-symptom relations.
Tariq King
STARWEST 2018 Reduce Wait Time with Simulation + Test Data Management
Slideshow

Data has become the most significant roadblock that testers face today. In fact, up to 60% of a tester’s time is spent waiting for data. Chris Colosimo shows that many factors contribute to this wait time, including internal requirements from the test data management team to pull data in the proper form, wait times for sanitized or “test-safe” data, or, most importantly, building data sets that do not exist. Compounding these challenges is the inherit complexity of today’s data. You have to be a DBA to even begin to understand the structure and relationships needed to support your testing. There has to be a better way! Learn how to solve these challenges by providing a self-service method where users can model and repurpose their data on demand. Discover how to use a test data assistant automation to capture, model, and generate data for efficient use in API tests and virtual services.
Chris Colosimo

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