"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.

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!

In this talk, I will speak about the process and strategies on how can one analyze and select the right tools for test automation. We will look at some tools and processes that help to select the tools, frameworks, libraries, etc.

I will demonstrate some helpful extensions and tools that will be very handy and useful during the process

This process can be carried out before every test automation project or engagement. The selection of the right tools will help throughout the process of automation and will help to avoid delays, changes, re-work, incomplete automation, etc. and will save a lot of time, effort and resources for the organization.

The session is designed for anyone in the Software Testing industry and the knowledge received from this session can be implemented right away in any of the upcoming or existing test automation projects.

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.

• Want to save 70-80% manual effort of automation script writing!!
• Wasting time in verifying xpath one by one?
• Want to complete automation script without wasting much time?
• Are you still wasting time in writing english manual test cases?

Selenium is a popular open source test automation tool, so popular it has its own conference event which run for 3 whole days across continents and is attended by hundreds of enthusiastic testers, and people wanting to know more about testing and automation. In my little experience of coaching for last 7 years, i feel what english language has done for common man[those from non english speaking countries] in earning bread and butter and giving them an elite status, selenium has done for manual testers.

Anyone and everyone wants to learn Selenium. The magic of selenium or the flexibility is that it can be learnt with your choice of programming languages. I have taught people Selenium with Java, C#, Python and Ruby largely in past these years. I wish to delve into the world of Javascript now and see what this has to offer to us. So this workshop is for those open minded souls out there who wish to pick up another language and learn, unlearn, relearn selenium with 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

Problem Statement:

As part of SDLC process:

1. Is your product quality impacted due to a smaller number of QA resources available in the team?
2. Are you waiting for QA resources to certify your code every time when you deploy? Is this impacting your product lead time (Speed to Market)?
3. Is your Product delivery timelines are impacted due to last minute defects identified?
4. Do you have your QA resources only in one location, but you want to “follow-the-sun” approach for Software delivery across multiple locations?
5. Do you have manual testers who are not skilled in programming, but you want them to execute automated test scripts w/o any training efforts and automation setup?
6. Would you like your team more agile and cross functional with Delivery?
7. Would you like to increase your QA team’s productivity while they invest more time in script development rather than script execution?

If answer is ‘Yes’ for above questions, "Zero touch automation" is the solution for above challenges that we have been facing part of SDLC.

Solution: Zero touch automation with cutting-edge technologies

In this session, I will cover how we solved this problem using innovative solutions, Cutting-edge technologies like NLP (Natural language processing), AI & Cloud solutions.

You will learn how AI, NLP integrated with core automation components to achieve Zero touch automation.

This solution is not just revolutionary, it is paradigm shift in test automation to get results to your email with detailed analysis of failure categorization with recommended actions to users.

I will also cover how E2E automation will be driven with decisions taken by machines based on what user is looking for . There is no manual intervention in this process. NLP and AI play key role to help machines to take decisions.

We will also cover how we empowered developer/release manager/any team member/Manager to trigger the scripts from their cell phone and get the detailed execution report without having any automation software installed in their computer or Phone.

We will be demonstrating how the request will be initiated from User, understand the need from user using NLP & AI , Fetching the code from bitbucket to select appropriate automation scripts , running them on Selenoid/docker server , storing results to MongoDB , receiving email with test results and Failure analysis.

What is the value of zero touch automation?

1. Enable speed to market: Now that Developers does not need to wait for QA resource, Changes can be certified quickly and ready to push to production. Lead time will be significantly reduced.
2. Increase quality: Now that test automation is easy and it can run multiple times in each environment, most of the defects will be uncovered and addressed before code goes to production.
3. Ease of test execution: Test execution will be very easy, no automation or framework setup required from user side. Test execution can be done 24*7.
4. Productivity: Increase QA team’s Productivity to focus more on script development rather than focusing on script execution and failure analysis .

If you are tired of waiting for your selenium tests status and old fasioned ways of reporting tests status, you are in a good place.

We will show you our test environment setup and how we run over 2500 tests in about an hour. Not cool enough? Take a look how we make reports via slack.

Do you want to check if we can be even more faster?

One of the biggest limitations with most of the frameworks in selenium is that we need to wait till the end to get the final test case report. Only then we can raise the alert that a particular functionality is broken. By then, it may be too late! In addition to this, in most of the frameworks, the HTML report generated is hosted on our company's private network and we need to connect to the VPN if we are outside the office to open the report and check the related screenshots to see which test case has failed and why.

So, why not raise an alert as soon as the test case fails? And why not attach a screenshot along with that alert so that all the stakeholders can actually see what has failed in the application?

In this session, we will learn a simple yet a really useful way of sending an alert along with the failed test case screenshot by integrating slack with selenium which will help us alert all the stakeholders as soon as an automated test case fails so that everyone can react to it immediately and the damage will be minimised.

We are now living in the age of Big Data. What makes Big Data so special is its ability to help the common man process petabytes of data to build innovative solutions. Cloud has played a pivotal role in building this data economy by making infrastructure more cost effective and enabling easy scalability.

Today, the abundance of data helps business make intelligent decisions through business process automation. Data drives all our digital interactions, starting from products that we see on our Amazon home page, to our Netflix recommendations, to the advertisement on Google search results, to even the Friends that we get connected to on Facebook. All these systems are powered by data that is collected from the various system, cleaned, transformed and stored into Big Data solutions. Testing of these data systems have become very complex and requires a new approach. This paper highlights various aspects of Big Data Testing including:

• Complexity in testing Big Data systems

• Cloud based tools for testing Big Data cross various stages

• Simplification of test processes and visualization through dashboards

• Testing Big Data in the Agile/ DevOps

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

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.

1. Are you Confident about your test coverage?
2. Do you have millions of permutations and combinations to be covered in testing?
3. is automation the solution to cover million combinations?
4. why can not my system decide what test cases to be executed to give complete test coverage?
5. As part of CI/CD , instead of executing same test cases irrespective changes going in, would you like to execute more appropriate test cases ?

I will be presenting quick demo on statistical approach and strategy which can help us to generate appropriate number of test cases automatically based on the impacted areas and business metrics . Let me share an example below.

Let’s say we are testing any eCommerce application Flipcart or amazon. We will have to cover multiple combinations like different categories of items, different payment types, shipping types, Promotions, user types etc.. . I will use little bit of sets theory here.

Let us take below 5 sets as an example. Assume that there is new payment type is getting introduced as “Paypal” as enhancement. If we need to make sure this new payment type is working with all products, all shipments types , all user types etc… we will have to cover all permutations and combinations which is nothing but Cartesian product of all these sets. The Cartesian product of below 5 sets is - 6*3*2*4*3 =432 test cases.

Do we really need to execute 432 test cases just for one new payment type ? we just need to execute 24 test case to give confident test coverage .This can be done with OATS tool which is developed with intelligence with Business user Metrics + Orthogonal array testing approach ( OATS) approach . OATS tool will generate required number of test cases with simple input and one click .

There are 2 key areas of input that we consider before we generate optimized tests.

1. Business user metrics - Example : if you look at the below payment set , we would like to know what % of customers using each payment type in overall successful transactions. let us assume 60% Credit card payments , 20 % paytm and 20% rest of payment types . These metrics will help tool to give more weightage to Credit card test cases over other payment types. This make sense because we need to focus more on the areas our end users are using.

2. Impacted areas: let us take an example that there is new payment type (AmazonPay) is getting introduced as en enhancement. We should give more weightage to payment set and make sure all existing payment types are tested with different combinations.

Set1 - PAYMENT = { Cash, Credit card , Paytm , AmazonPay, Debit card}- # elements - 6

Set2 – SHIPPING – {Shiptoday, Prime Shipping, ship Tomorrow}-# elements - 3

Set3 – USER TYPE - { Guest User , Logged in user }-# elements - 2

Set4 – PRODUCT TYPE – {Electronics, Groceries, Books , Stationary}- # elements - 4

Set5 – PROMO TYPE – { Promo1 , Promo2 , Promo3}-# elements – 3

I will be covering how this can be done in detail during presentation along with Tool Demo which was built in with an intelligence using sets theory . you will get an answer on why only 24 test cases are good enough instead of 432 test cases for the change new payment type got introduced.

Advantages :

1. Optimized coverage with less test cases

2. Tester will be confident about test coverage

3. This tool output can be integrated with Selenium key word driven framework . All this happens during run time w/o manual intervention.

4. This strategy is good fit for automation testing .

5. if any combinations are invalid to test, those combinations will be excluded while generating tests.

6. There are tools in the market to provide statistics about untested code (Ex: JaCoCO ). These statistics can be used as input for OATS tool. We have not done this yet.

Limitations:

1. We have seen great value (reduced bug escape) applying this strategy to automated tests. However there is limitation in applying the same strategy to manual exploratory , Adhoc testing.

2. We have not tried this strategy for performance testing yet.

3. There are corner scenarios where human can only think will not be covered by this tool . These scenarios to be appended to generated test cases .