test automation

Conference Presentations

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
STARWEST 2018 How to Automate Testing for Next-Generation Interfaces (BOTs, Alexa, Mobile)
Slideshow

Today’s IT systems communicate with customers through multiple points of engagement and various interfaces, ranging from web, mobile, and voice to BOTs and apps like Alexa and Siri. Sanil Pillai says these systems need to provide seamless handoffs between different points of interaction—while at the same time providing relevant and contextual information quickly. To accomplish this, a team must be able to successfully pair device hardware capabilities and intelligent software technologies such as location intelligence, biometric sensing, and Bluetooth. Sanil shows that testing these systems and interfaces is becoming an increasingly more complex task, and traditional testing and automation processes simply don’t apply to new-generation digital interaction services. Join Sanil as he discusses the testing and automation challenges in new-generation digital interactions using hyperconnected BOTs.

Sanil Pillai
STARWEST 2018 Marrying Artificial Intelligence with Software Testing: Challenges & Opportunities
Slideshow

Emerging technologies such as the internet of things (IoT) and cloud computing have introduced a significant software variety and complexity. Wendy Siew Wen Chin and Heng Kar Lau explain that testers are challenged to support a wide product portfolio within harsh time, resource and budget constraints. More test automation may seem to be a solution to test efficiency, however there are many inefficient hot spots throughout the test automation life cycle. Join Wendy and Heng Kar as they share their experiences from the Intel IoT team. They share how to make use of artificial intelligence (AI) tools to leverage opportunities throughout a testing project. They show how to blend test data analytics, test automation, test coverage analytics and test case selection. Learn how software testing, AI and data analytics can be combined to transform your testing, by helping you focus your testing on what matters most.

Wendy Siew Wen Chin
STARWEST 2018 Mission Critical Automation Testing
Slideshow

When critical subsystems fail, the resulting losses can be catastrophic. In the insurance industry, if premiums are miscalculated, defect costs can reach well over a million dollars. In this session, Mike Keith and Dom Nunley draw on their practical experience with insurance systems testing to provide an overview of combinatorial automation testing for high-risk backend system areas—i.e., features that absolutely must work correctly. They share a process for categorizing requirement risk levels to determine which requirements warrant combinatorial testing. Mike and Dom illustrate various combinatorial testing techniques such as N-FAT, N-Wise, and RANDOM, which can be used to automatically generate test cases. These methods are used to ensure coverage against risk while controlling the number of tests that run.

Mike Keith
STARWEST 2018 Everything I Learned 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 Doo®, Wile E. Coyote®, and many other favorites! Like Bugs Bunny®, maybe we should “have taken that left turn at Albuquerque” and possibly done things a little differently. Discover how the animators in Spiderman® didn’t redraw every background but reused the animation cells, similar to our reusing pieces of test code. And see how Scooby Doo taught us that with the right team, we can solve anything! Chris talks about the automation that he is building at InGenius and how all those hours in front of the TV are helping make it successful. Come for the ‘toons, leave with the lessons!

Chris Loder

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