Writing code is easy, but writing good code takes a lot of practice and hard work.

Automating tests is easy, but identifying and automating the "right scenarios" takes experience, understanding of the big picture and then automating the same. Similarly, in many products, there are multiple "user-personas / roles" involved in completing a scenario. However, we end up automating only the single-user scenarios, as that is easy. But this approach does not provide enough ROI from your automation perspective.

In this session, we will share the approach taken to automate tests simulating the real-world, multi-user scenarios, where each user could be on a different platform (Android, iOS, Windows Desktop, Web) and the test orchestrates the interaction between these personas.

This approach is based on open-source tools and frameworks - Selenium WebDriver, Appium, AppiumTestDistribution and teswiz.

Writing code is easy, but writing good code takes a lot of practice and hard work.

Automating tests is easy, but identifying and automating the "right scenarios" takes experience, understanding of the big picture and then automating the same. Similarly, in many products, there are multiple "user-personas / roles" involved in completing a scenario. However, we end up automating only the single-user scenarios, as that is easy. But this approach does not provide enough ROI from your automation perspective.

In this session, we will share the approach taken to automate tests simulating the real-world, multi-user scenarios, where each user could be on a different platform (Android, iOS, Windows Desktop, Web) and the test orchestrates the interaction between these personas.

This approach is based on open-source tools and frameworks - Selenium WebDriver, Appium, AppiumTestDistribution and teswiz.

Ever happened to you that your UI gets migrated to the angular stack while you have all the automated tests and infra in place in selenium Webdriver and Java framework. When you run your tests they are mostly flaky and and drain your efforts in fixing/false negatives?

Is the solution to this problem finding a new tool that fits well for angular UI such as protractor? Or to spend some time refactoring your existing framework to support angular UI and leverage the existing tests and infra in place?

In this talk we shall discuss the later solution and the implementation.

Last year was dominated by the smart devices & voice based home assistants. These use the conversational interfaces unlike other applications to interact, built using advanced algorithms, ranging from natural language processing to AI/ML techniques. They are constantly learning by themselves improving the interactions with the user bringing up the challenge of non-deterministic output. To such interfaces, natural language is the input & we humans love having alternatives & love our synonyms, express using emojis gifs & pictures. Testing in this context moves to clouds of probabilities.

Unfortunately Selenium cannot be used to automate such systems and hence Botium was designed.
In this session I will cover the Selenium driver for Botium to automate E2E tests on Web UI and mobile along with testing strategy, testing NLP models & automating these tests to the CI/CD build pipelines with a DialogFlow based 'Coffee-Shop bot' as an example during my demo.

With the fast paced world it’s very important to make the decisions faster and that can be possible only if you have a good test coverage and a faster feedback.
One can use selenium grid on the local systems and achieve the cross browser and device testing which may have constraints with respect to scalability and maintainability, or use SaaS alternatives such as BrowserStack/Saucelabs which fit well for lesser no of tests (gets a bit expensive as we get into concurrent sessions and increase the browser/device combinations) or moreover containerize the automation having the limitations with coverage of IE. How to design a solution to get the a scalable, on-demand, manageable and yet cost effective infra? Selenium Grid along with Terraform and AWS can be leveraged to write the whole infrastructure as code to prevent any human errors and manual intervention. In this talk, I shall take you through the journey of reducing the test execution pain-points in terms of time, resource availability, conflict handling, scalability and state management with a demonstration.

Last year was dominated by the smart devices and voice-based home assistants. These use the conversational interfaces unlike other application to interact with. They are built using advanced algorithms, ranging from pattern and expression matching engines to natural language processing and AI/Machine learning techniques. These systems are constantly learning by themselves improving the interactions with the user bringing up the challenge in the testing world of non-deterministic output. To such interfaces, natural language is the input and we, humans really love having alternatives and love our synonyms and our expressions using emojis gifs and pictures. Testing in this context moves to clouds of probabilities.

In this session I will cover the strategy for testing such interfaces, testing the NLP models and sharing experience on how to automate these tests and add it to the CI/CD build pipelines.

Key learning:

• How What and why of a conversational interface?
• How can I build my testing approach for such an interface?
• What from my current toolset can I use for this new context?
• How do I automated and add it for my CI/CD pipeline for instant feedback?
• How do I measure the quality?

Chatbots are one of the most widely adopted AI/ML implementations in the business sector. A chatbot is an intelligent machine used to imitate human conversation through text and voice commands. Today bots are widely used as a personal assistant, customer service, HR, sales and marketing to name a few. In short, bots are everywhere and we rely on them to a certain extent, this makes it extremely important to assure the quality of the chatbots and test them thoroughly. They are built using NLU/NLP-Services (Natural language understanding and processing) and are subjected to constant training and improvement which has direct impact on tests. Voice based bots like Siri and Alexa depend on speech recognition technologies. As the chatbots user do not have any barriers and due to the unpredictable user’s behavior it becomes utmost difficult to verify the correctness on the output. In this talk, we will discuss how the chatbots are different as compared to the other applications and the challenges they bring onto the table while verifying their behavior, and focus on the testing strategies and automation testing of the bots.

Chatbots are one of the most widely adopted AI/ML implementations in the business sector. A chatbot is an intelligent machine used to imitate human conversation through text and voice commands. Today bots are widely used as a personal assistant, customer service, HR, sales and marketing to name a few. In short, bots are everywhere and we rely on them to a certain extent, this makes it extremely important to assure the quality of the chatbots and test them thoroughly. They are built using NLU/NLP-Services (Natural language understanding and processing) and are subjected to constant training and improvement which has direct impact on tests. Voice based bots like Siri and Alexa depend on speech recognition technologies. As the chatbots user do not have any barriers and due to the unpredictable user’s behavior it becomes utmost difficult to verify the correctness on the output. In this talk, we will discuss how the chatbots are different as compared to the other applications and the challenges they bring onto the table while verifying their behavior, and focus on the testing strategies and automation testing of the bots.

Chatbots are one of the most widely adopted AI/ML implementations in the business sector. A chatbot is an intelligent machine used to imitate human conversation through text and voice commands. Today bots are widely used as a personal assistant, customer service, HR, sales and marketing to name a few. In short, bots are everywhere and we rely on them to a certain extent, this makes it extremely important to assure the quality of the chatbots and test them thoroughly. They are built using NLU/NLP-Services (Natural language understanding and processing) and are subjected to constant training and improvement which has direct impact on tests. Voice based bots like Siri and Alexa depend on speech recognition technologies. As the chatbots user do not have any barriers and due to the unpredictable user’s behavior it becomes utmost difficult to verify the correctness on the output. In this talk, we will discuss how the chatbots are different as compared to the other applications and the challenges they bring onto the table while verifying their behavior, and focus on the testing strategies and automation testing of the bots.