A highly regarded speaker in the conference circuit and luminary in the software testing world, she approaches the challenges of quality assurance with deep insight. All of that has come together into my main interest at the moment: The UX and usability of testing tools for testers. Isabel: In the 70s or 80s, someone famously wrote, "Don't talk about computer interfaces; all interfaces are human interfaces." At the same time, development has gone from small focused teams working on a specific problem through to big projects with silo working and now coming back to people saying they need more frequent deliveries -- essentially, the rise of Agile and DevOps.
Bots and AI have affected software testing and development in terms of testing scope and workloads, debugging adequacy, and advanced continuous testing. Software testers can have a full team of robotic test automation running a wide scope of tests and make it their task to oversee, examine, and assist them in programming the testing procedure. Utilizing artificial intelligence in robotics to advance continuous testing can expand the extent of ongoing testing capacities. They may not exactly be here yet, but the use of artificial intelligence in software testing quality and reliability is coming very soon.
Welcome to the world of machine learning in software testing. Machine learning in software testing requires an entirely different approach. Testing these systems requires a deep understanding of the problem domain and the ability to quantify the results you need in that domain. For machine learning in software testing, you should also have a high-level understanding of the learning architecture.
Essentially, machine learning refers to computers or software platforms "learning" over a period of time. Other examples of machine learning include Google's self-driving car and the way websites deliver targeted ads to customers. For computer developers, machine learning has evolved considerably. Nothing is perfect, and modern machine learning systems and processes are no exception.
But will there ever be a role for artificial intelligence in software testing, aka a machine software tester? Once that's possible, we combine machine learning about fields -- for example, fields named "first_name" have this common set of valid inputs: John, Michelle, Sarah, Robert -- with the model-driven techniques to take random walks through an application -- and we can have an army of machines with inductive expertise testing our software overnight. Most visual test tools allow their users to train the software to ignore fields that change all the time -- automatically generated date fields -- or to only focus on things that shouldn't change. Even with a machine software tester, visual inspection tools still need a human to run them.