"This world is such a beautiful place to live in." If you can read the first sentence without any screen readers or assistance, you're privileged. As technologists, shouldn’t we be more empathetic towards differently-abled people and make all parts of our website accessible to them? In my humble opinion, the true power of technology can be identified when it reaches out to people of all kinds having different physical or psychological challenges. We not only legally bound to provide Accessibility but also it should be considered as our moral responsibility.

As testers, we have a wonderful opportunity to contribute to Accessibility by ensuring that the site is accessible in many different ways. Although it is impossible to identify all the issues that exist around Accessibility in the world, we are lucky enough to still understand a majority of them. With this understanding, there have been many measures taken in order to make your site accessible. But, don’t forget - we are QA engineers. We got to ensure that the site is accessible as per the standards set by WCAG 2.0 (AA) by testing for accessibility using various tools and techniques.

Browser tests are known to be the flakiest ones. This is partly because browser infrastructure is complicated to maintain. But the second reason is – mainstream browser automation tools such as Selenium server are far from being efficient.

During my previous talks I was speaking about Selenoid - a truly efficient replacement of the standard Selenium server. This year I would like to do a live demonstration how to organize a fault-tolerant and easily scalable Selenium cluster using virtual machines in the cloud. I will start by setting up Selenoid and show its powerful features like video recording, live tests debugging, manual testing and many more. Then I will configure Selenoid to send logs and recorded videos to S3-compatible storage. Finally we will run a Ggr load balancer instance allowing to use all running Selenoid nodes and organize a single entry point to the cluster.

We learn more from our failures than our successes. I want to share one of my failure stories, where I learned that coding is not the most important task in Test Automation.

A failure taught me that coding is not the most important task in Test Automation. I fixed a bug, added tests, and shipped it to production. After that, a bug report came in showing that the fix created a new bug. I felt bad, I did not understand how that happened. A hotfix solved the issue, but the root cause was not addressed: what did I miss?

By taking a step back, I understood the situation, it all happened by overseeing basic concepts in testing (like understanding how the system works). The problem was that coding had more priority than creating a test plan. After this, I prioritized tasks better to avoid this situation to happen again.

This talk shows that testing concepts are more important than ever, in a time where tools promise to do everything, we focus less on what and how to test, and more in using tools to test. It outlines how a test strategy can leverage a continuous testing setup. Finally, it shows that failing is ok, but failing again for the same reasons is not.

A recent study conducted by Gartner, shows that by 2020, AI will be pervasive in almost all software products and services. The major highlight of the study was how our skills as engineers would have to adapt accordingly. The current roles in companies are going to change significantly and we need to be prepared for it. There are areas of healthcare, finance, retail, customer analytics, and manufacturing that have already started seeing positive effects of using AI, many of which we would have never thought possible just a few years ago. At the same time, we have downsides of using AI, such as how it can easily impact our data and privacy if we do not pay attention to how the AI models are trained. The working of AI is a black box-- we do not control or understand how the algorithm forms different relationships and makes decisions -- we just provide different training datasets and monitor the learning/progress. We are trying to make predictions on future values based on learning from past examples, or trying to discover different patterns from datasets. This being the case how do we approach these systems?

In this talk, I will discuss the basics of how AI based systems work, what are the key factors to consider when developing and testing these systems and how to come up with an enterprise wide strategy. I will provide insights into what skill sets would be needed by developers and testers to co-exist in the world of AI. Finally, I will address some common questions and concerns surrounding AI and also show what are the tools available for doing AI based development and testing. This talk will help anyone get a basic understanding of developing and testing AI based systems and make us aware of the key factors to consider to ensure these systems do not have a negative impact on humans.

Key Takeaways-

• Learn with live demos how AI model training happens
• Learn how AI is used in software development and testing
• Learn what AI based tool and frameworks are available for software development and testing
• Learn key factors to ensure these systems are safe for humans

While working on our daily tasks in agile teams, we quite often have this feeling where we are working on multiple tasks all day long and at the end of day when we review our work, we realize we haven’t accomplished anything concrete. The main reason for this is, our work environments are filled with distractions from having unnecessary and unproductive meetings to people constantly checking their messages on phones, emails and slack channels. As a result, we feel demotivated, less productive and burnt out by the end of the day. Burnout is a serious issue and is attributed to a lot of mental and health issues. I was one such person who after working around the clock and helping people 24/7 ended up getting admitted to the hospital. How do we prevent this repetitive cycle from happening on a daily basis? This is where mindfulness and productivity hacks can help.

In this talk, I will share my real life experiences from experimenting with different mindfulness and productivity hacks which ended up helping me get more focus, regain my mental and physical health and most of all made me fall in love with technology all over again. Some of these hacks include doing deep work, different tips and tricks to prioritize tasks, deal with emails, make meetings more productive, effective engagement with remote teams and much more. Come join my journey and become the best version of yourself.