"All tests in today's automated regression run have been marked as Untested. What happened?"

"No notifications are being sent for test runs on the channel"

"I pulled latest code, and the framework dependency shows compilation error"

"What does this new method in the framework do?"

How often do you hear such things within your team?

As Quality champions, we need to walk the talk. When we expect our developers to write quality code, write unit tests, build features without introducing bugs, the onus lies on us (as test engineers) to do the same. With almost every test engineering team writing automated tests to check functionality of their products and services, it becomes very important to ensure that the test automation framework and the test scripts are bug-free and follow good standards of software engineering.

It cannot be stressed enough that test automation code should be as good as production code. In order to build production-quality test automation framework and scripts, a number of steps can be taken at:

1. Code & System Level

2. Process & People Level

Our test engineering team went through a transition from having random & unexpected failing test runs to having greater confidence in the quality of the tests. Learn from this case study of our journey to ensure that end-end UI automated tests are built with quality in mind. We will also see demonstration of some of the use cases.

With an ever increasing no of businesses being conducted on web the testing need to write automated tests for the app's UI is something that can never be ignored. As you all know Selenium provides an API that enables us to do this quite effectively.

However, when tasked with setting up the automation framework, there are a lot of questions that arise in the minds of aspiring test developers regardless of what level they are in their career.

Some of such questions are:

1. How does one actually go about the business of building a robust and effective automation framework on top of selenium?
2. What are the elementary building blocks to include in the framework that an aspiring automation developer should know of?
3. How should we model our tests? XUnit style vs BDD?
4. Are there good practices, sensible design patterns and abstractions that we can follow in our code?
5. What are some of the anti patterns/common mistakes we should avoid

A lot of literature, documentation and blogs exists on these topics on the web already.

However In this talk,

I would combine this existing knowledge and my years of experience in building automation frameworks and breakdown these elements and walk you through exactly the sort of decisions/considerations and practices that you can take while starting to implement or improve the UI automation for your team.

Hope to see you there!

So you have got a few UI tests and they are running in parallel, great! However, life will not be so sweet once these 'a few' turns into 'a lot'. We grew from a few to 1500 UI tests (although not particularly proud of this number, there are situations and reasons)

We started with a simple parallel distribution of tests 3 years ago. As test count increased failure count run time increased along with increased flaky tests. Mobile tests had their own challenges (eg. device dropping-off, random wi-fi issues, etc) To keep up with this, we created a queue and workers based solution which could distribute the tests more efficiently (https://github.com/badoo/parallel_cucumber). Over time, we made more improvements, in particular:

• Segregation of failures based on infrastructure issues and re-queue the tests
• If a device/emulator malfunction, rescue the tests to another device
• Repeating a single test on 100s of the worker in parallel to detect flakiness
• Repeat a test if a known network issue
• Terminating the build early if more than a certain number of tests have failed
• Health check of each device, before each test to ensure reliability
• Muting a test if failure is known, and highlight outdated mutes if the related task is fixed

In this talk, I will talk about the initial challenges with running UI tests in parallel (Selenium and Appium), how we approached the queue based solution and continuous improvement of this solution; finally, how attendees can use it at their workplace or create their own solution based on our learnings.

There are dozens of matured docker based solutions available in the market for Android automation testing,

but

what about iOS testing?

It’s hard to create and maintain the docker based solution for iOS testing since it demands the Xcode and for the optimal performance the system needs the Apple certified hardware.

Combining Ansible with Selenium Grid yields a powerful combination because it allows us to set-up our grid and nodes in just a few seconds. In this talk I will demonstrate how to use simulators to build a reliable and scalable in-house iOS simulator lab using Ansible, Selenium Grid and Appium.

There are already over 200 million users consuming VR applications by 2018. And with Google, Mozilla pushing WebXR capabilities in browser and vendors like BBC, Amnesty International, Universal, Disney, Lenskart and a lot of them adopting them to their websites, we will soon see a huge rise i demand for Web VR and Mixed Reality applications.

But how do you test them in scale? How do you define "smooth" as opposed to just responsive?

In this talk I will go over some key details about the WebXR specification. The work that Mozilla, Google and the W3C Immersive Web Group is doing. The differences between testing a regular web page and a Mixed Reality enabled one. What to watch for and how you can automate it.

It can be intimidating for inexperienced teams embarking on their test automation journey for an existing code base. There is so much to test, and so many ways to test. I often see teams stuck with debating on where to start and what tools to use and best practices:

"We should start from unit tests"

"No, integration tests are better!"

"Should we use tool A or tool B?"

I see this play out all the time, and I've been there before. And the worst that could happen is decision paralysis and inaction.

The bigger question really is "What to test?".

My rule of thumb is "Start with the scariest code". I'll share with you my framework for evaluating the ROI of writing a test for a feature and prioritising what to test.

Most testers follow the career path their organisation is offering them. Sometimes, this path seems dated. Specially when one compares them to other organizations which seem to be taking testing more seriously or are atleast more advanced in their approach to people and technology.

What could be the possible options beyond a certain point as a tester? Will present certain questions to the testers - What do you enjoy doing most? What comes naturally to you? How does the competition look like? What could you potentially learn? What are the alternates or extended skills you enjoy - data science more or engineering more or something else? What are your super powers? What could be the path of least resistance to make a shift. Will share leading examples of some "popularly successful" career.

In the 2nd half of the talk - will get the audience to engage to understand the designations and potential career paths that interests them. Will discuss out specific ambitions and goals and share what has worked for others and what could work for them & how to go about it.

Problem Statement:

While running thousands of automated test scripts on every nightly test schedule, we see a mixed test result of pass and failures. The problem begins when there is a heap of failed tests, we caught in the test-automation trap: unable to complete the test-failure triage from a preceding automated test run before the next testable build was released.

Deep Learning Model:

The classification was achieved by introducing Machine Learning in the initial months and followed by Deep Learning algorithms into our Selenium, Appium automation tests. Our major classification was based on the failed test cases: Script, Data, Environment, and Application Under Test and that internally had hundreds of sub-classifications.

To overcome this problem, we started to build and train an AI using Deep Learning, which simulates a human to categorize the test case result suite. Based on the test result failure for each test, the AI model would predict an outcome through API, categorizes and prioritize on the scale of 0 to 1. Based on the prediction model, the algorithm takes appropriate response actions on those test cases that are failed like re-run test again or run for different capabilities. We kick-started this by gathering the historical data set of 1.6 million records, which was collected over a 12 months period, data including the behavior of the test case execution and the resulting suite.

This Deep Learning-based algorithm has been provided the quality to break down the new defects based on its category, and a classification score is given on a scale of 0-1. We’ve also established a cutoff threshold based on its accuracy of improving, and to group the failed test cases based on its similarity. Classification of the test cases is done in high granularity for sophisticated analysis, and our statistical report states that the classification of the defects has been increased with 87% accuracy over a year. The system has been built based on the feedback adapting models, where for each right classification it will be rewarded and for the wrong, a penalty is given. So whenever receiving a penalty the system will automatically enhance itself for the next execution.

The algorithm has a powerful model for detecting false-positive test results calculated using the snapshot comparisons, test steps count, script execution time and the log messages. Also, the model has been built with other features like – duplicate failure detection, re-try algorithms and defect logging API, etc.

The entire classification system has been packaged and deployed in the cloud where it can be used as a REST service. The application has been built with its own reinforcement learning where it uses the classification score to enhance itself and this is programmed to perform in an inconclusive range.

In sum, this deep learning solution can assist all Selenium testers to classify their test results in no-time and can assist to take next steps automatically and allow team could focus its efforts on new test failures.

Link: https://github.com/testleaf-software/reinforced-selenium-test-execution

The best engineering teams are releasing code hundreds of times in a day. This is supported by a test setup that is not just fast, but robust and accurate at the same time.

We look at data (anonymized) from millions of tests running on BrowserStack, to figure out the very best test setups. We also analyze the testing behavior of these companies—how do they test, how frequently do they test, how many device-browser-OS combinations do they test on. Do they gain speed by running more parallels or leaner test setups?

Finally, we see how these steps help these teams to test faster and release continuously, and how it ties-in to the larger engineering strategy.

Are we investing our efforts in building things that actually matter? Is the new feature that we rolled out adding value to the customer? Is the new release doing better than the previous releases? How do we get answers to these questions and more? Analytics is our answer!

Analytics information helps not just the business teams but also QAs, Devs, PMs and other members of the project in multiple different ways. It could help us uncover some critical issues, it could help us understand customer sentiments better, it could help us get a broader picture of how the customer actually uses the product and whether it was how we intended it to be, it can help us get ideas about what small or large changes the customers are looking for without them having to explicitly tell us.

Analytics is important information to us. So, it is also critical that the information is correct. That means analytics information produced also needs to be tested and validated.

This talk is intended to understand the testing of analytics events and why they are important to us.

In this talk, we will cover our experience of how analytics information helped us understand our customers better and invest our time in building the right things. We will also cover how we validated it to ensure that the data that we were seeing was actually correct. In addition to this, we will also briefly cover some details about other sources of information that can be looked at if we are working in a mobile world.

Please note: We’re open to tune the proposal based on feedback

Everyone starts off with a simple grid setup which involves a hub and one or more nodes.

This traditional setup is a good start but the moment one starts to get serious with the selenium grid and decide to house their own selenium grid for their local executions, that is when issues start.

My experiences with the Selenium grid in the past couple of years has led me to get introduced some of the most prevalent problems with maintaining an in-house selenium grid.

• Nodes get unhooked randomly due to network glitches.
• Nodes introduce false failures due to memory leaks.
• Selenium Grid running out of capacity.
• Nodes require OS upgrades/patches etc.
• Needing to deal with auto upgrades by browsers (especially chrome and firefox)

Some of these issues I managed to fix by building a "Self Healing" Grid wherein the nodes automatically get restarted after they have serviced "n" tests. But that still didn’t solve many of these other problems.

That was when I felt, what if there was an on-demand selenium grid.

What if the Grid could do the following ?

• The Grid auto scales itself in terms of the nodes based on the current load.
• The Grid does not require a lot of infrastructure to support it.
• The Grid can plug itself into some of the cloud providers or leverage a solution such as Docker so that the nodes can be spun and shutdown at will.

That was how the idea of "Just Ask" an on-demand grid was born.

Just-Ask is an on-demand grid. It has no nodes attached to it.

It’s designed to spin off nodes on demand, run test against the newly spun off node and after test runs to completion, clean-up the node as well. The node can be backed by anything. It could be Docker (or) it could be a VM running on any of the popular clouds.

The session aspires to walk the audience through with my experiments with the selenium grid, my learnings on the selenium grid internals and how I used all of that knowledge to build my own On Demand Selenium Grid. What better avenue to share these learnings than a Selenium Conference.

The session will introduce the audience to the grid internals and their concepts such as

• What is a Selenium Remote Proxy ? What is it used for? What can you do with it?
• What is a Hub (or) Node level Servlet ? When would you need one ?
• All of this followed by a quick demo on "Just Ask", the on-demand grid that I have built and open sourced here: https://github.com/rationaleEmotions/just-ask

Virtual mobile devices (emulators/simulators) are a cost-effective and straightforward alternative to testing on physical devices. We showcase how to set-up and scale an Android emulator farm using Appium, Docker and SQS and how it fits into our larger testing and quality strategy.
Maintaining physical test devices for mobile automation can be expensive and time-consuming. On top of the initial investment, you need to consider maintenance cost, replacement devices and efforts for manual scaling. On the other side of the spectrum, cloud providers take care of these restrictions, but their services can come at a hefty price tag, especially when your use-case requires a large number of devices. We present a middle path and demonstrate how to use virtual devices to build a reliable and scalable in-house device lab using Docker and Appium.

Testing for StartUps is very different than testing for big businesses. Mostly, because of the lack of funds and time to market – which are common constraint across the globe. But most importantly, because the startups are mostly pivoting, if not completely, then in parts. Their feedback cycle have to be super-efficient and really quick. Users are directly involved and lot of monitoring and user metrics decide the way forward for the product.

When testing for such fast moving startups , one needs to strategize smarter with their time, funds and tools. The approach taken is indeed out of the box sometimes. Unlike, existing businesses where the end goal is defined, startups are lot of times open to a new end, which is more glorious than what they had originally planned for. (This may sound like machine learning has been inherently applied.)

The ideas and responses will be radically different and pretty disruptive, for some routine questions for developing the test approach, in case of a startup- Why are we testing, what are we testing, Who will test, what will get impacted, how to test, which tools can help, what metrics are the business measuring, how much time do we have….

Learn to test for startups in the coolest and most efficient way. Be useful to your business.

A key requirement for successful test automation is to get past the brittle or fragile nature of test scripts. Any Selenium (or Appium) developer has encountered the dreaded "NoSuchElement Exception". A locator that worked yesterday may fail today. What's a test engineer to do?

Fortunately the field of AI provides promising solutions and allows for the creation of self-healing tests. Tests that can find elements across all environments. Tests that can learn from "human-in-the-loop" intervention and work perfectly thereafter. Imagine automated tests that "just work"!

This session will look at how to apply the latest in AI and Machine Learning technologies to improve your test scripts. With the plethora of new open source AI libraries made available by companies such as Google, the ability to leverage AI in your applications is more accessible than ever.

This session will be a primer on AI technologies and how they can be utilized for perfect test automation.