CAST 2021 Speaker Highlights (1)

See who's speaking at CAST 2021 in Atlanta!
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Tariq King
Testing AI and Machine Learning: A Holistic Strategy
Although there are several controversies and misunderstandings surrounding AI and machine learning (ML), one thing is apparent - people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Testing ML is challenging and requires a cross-section of skills, and experience from areas such as mathematics, data science, software engineering, cyber-security, and operations. In this tutorial, Tariq King introduces you to a holistic strategy for testing AI and machine learning systems. You’ll start with AI/ML fundamentals and then dive into approaches for testing these types of systems offline, prior to release, and then online, post-deployment. Engage with other participants to develop and execute a test plan for a live ML-based recommendation system, and experience the practical issues around testing AI first-hand. By the end of the session, you’ll be able to better prepared to help your organization build trustworthy machine learning systems.
Ben Simo
Computer-Assisted Testing
An experience report.
People often classify testing as being either manual or automated. Some even label testers (including themselves) as either manual or automated. This is a peculiar distinction, and a false dichotomy.
Programmers don’t describe themselves or their work as manual programming or automated programming. Programmers use automation, and they use their hands; but most importantly: they use their minds. They use their minds to analyze human problems and develop software solutions to those problems. In the same way, software testers use their minds to analyze problems and solutions in order to design experiments that demonstrate how software works and uncover problems. Testing cannot be automated any more than programming can be automated.
Rajni Hatti
Ethical Hacking for Testers
Security testing is often handled by a specialized team or a set of automated tools, but every tester should understand the basics of how malicious data can enter a system in order to prevent the vulnerabilities from occurring in the first place. In this session, I will share a case study from a healthcare technology project where I led the QA initiative. I will show you the ethical hacking practices I used to find security flaws in various contexts such as design, code review, testing, and product release.
Greg Sypolt
Building a Better Tomorrow with Model-Based Testing
Let’s build a better tomorrow and a more equitable world.
We have all heard the call for change, and EVERFI is committed to answering it with educational courses and quality digital transformation with model-based testing. In a world of agile development, we experience a fast-paced development environment that is challenging, if not impossible, to keep up with hundreds of courses that require testing across our platforms. It calls for change, and EVERFI is committed to answering it by introducing model-based testing.
Tariq King
Towards Better Software: How Testers Can Revolutionize AI and Machine Learning
You may have heard that software ate the world and AI is eating software. However, if you’ve been paying attention, then you’ve probably realized that the world is filled with bad software. Many organizations struggle with meeting their quality goals and keeping testing-related costs contained. Software is indeed revolutionizing the world, but the world is also paying a revolutionary price for bad software. So where do AI and machine learning (ML) fit in? Are AI/ML breakthroughs just new ways of filling the world with bad software? Or do they offer a path towards better software?
Laurie Sirois
Quality Isn’t Funny
How can software testing professionals help their organizations take Quality even more seriously? Many of the challenges testers face are consistent across industries. This humorous, memoir-style talk will bring light to such serious topics as: elevating quality professionals to 1st class citizen status; getting buy-in on “Shift-Left” testing practices & true continuous improvement culture; how to add value by identifying risk, representing the customer, honing requirements and planning testing efforts earlier in business discussions; and how to sustain progress by dropping enough of our defenses to take ownership to the next level (organizational).
Jack Taylor
When to Say No to Automation
I work for a large Fin Tech company in which the higher ups base their lives on scorecards and eye grabbing headlines. “The Travel Team now have 100% Automation and have completed their implementation of DevOps!” or “We are currently at 75% automation but we hope to have 100% across all applications by the end of Q2!” are typical phrases you’ll hear, but is this culture counter productive when it comes to testing?