AI & ML in the QA Industry: Friday 11/20/20 10am, PST
On November 20, 2020, Women in Big Data kicked off the “Better Data for Better App” series with Jonathan Lipps, on “AI and ML in the QA Industry”.
Jonathan is the Director of Learning at HeadSpin and focuses his time in three key areas:
The talk covered a topic that marries two things – QA & AI and the promise AI holds for various industries and some of the applications of ML in the QA industry. Jonathan spoke in depth about what some of the products that have been marketed to the QA Industry as AI have been and evaluating it from a lens of a QA expert as to what is myth vs. reality.
The Software development cycle consists of the following stages:
Ideate Feature ⇔ Build Feature ⇔ Test App ⇔ Release App ⇔ Evaluate user response
This is an iterative process where you are improving a product and coming up with new things based on how things went with your last iteration. QA Industry is about Quality assurance and Quality control, figuring out what works and what doesn’t. In software development, the testing stage of the cycle is owned by the QA team.
Types of QA
When people are talking about applying AI/ML to the QA Industry, they are usually talking about processes and ML methods that assist with automated QA or maybe replace automated QA. Manual QA is becoming obsolete due to the need of speed and efficiency in the software development cycle.
The AI question: Sometimes we can go a little bit overboard in thinking how AI is applied to actual problems we want to solve. Is AI == BS?. Sometimes what people promote as AI is really Software development under the hood; it’s not anything different from what we’ve had for years in coming up with algorithmic solutions to problems: hand-coded solutions to problems that use regular old software development practices.
Categories of AI solutions in QA
Do you need “AI” in your testing? Why?
Jonathan believes one way to answer this question is to evaluate technologies based on their actual ROI, not how well they claim the hype of the zeitgeist. Also an important question to ask when doing your diligence on a product that claims to be AI is: what corpus was used to train the ML Model? Jonathan believes that most ROI for the products will be from AI/ML in supporting roles for a while.
Thank you, Jonathan! Grateful for your time.