Shadow DOM is kind of web standard that developers use to encapsulate their custom HTML code and style components so that no other global style components can override their code. It ensures that a component will work in any environment, even if other CSS or JavaScript is run on the page i.e. Shadow DOM hides away the complexity of the components from the rest of the page because it renders separately from main DOM.

Selenium legacy selectors are not able to interact these custom elements under the shadow DOM so we need java script to interact with shadow DOM elements and we also require JavascriptExecutor interface for their execution.

We will also talk about the WebdriverIO tool v5.5.0, which implicitly support Shadow DOM elements.

The adoption of Artificial Intelligence is getting more traction, it is in need to enhance QA capabilities to cope up with these skills. Machine Learning is used extensively in retail applications for solving complex problems, one of them is solving the search relevancy. Showing the appropriate results for the user is important for the conversion rate to go high. As Machine Learning poses different challenges such as a Test Oracle, Fairness, Correctness and Robustness to do QA, We may need to follow different approaches and testing techniques to do the QA for Machine Learning models.

Different Machine learning types such as Supervised and Unsupervised Models have different characteristics and are used for different types of problems. Though these solves different complex problems, Machine learning Models also a unit of software code that needs to be verified as a normal software system. When a Machine learning model is seen as a whole system, it may look complex and unsolvable. We can group them into small modules and verify for quality. Black box and White box testing techniques can be applied to verify the functionality. Data, Feature Engineering and Algorithms are the major part of the Machine Learning model. We will see how we applied different techniques to validate these.

This talk is focused on viewing the Machine Learning software as a whole and performing the Quality Analysis for it. We look at how different is testing a machine learning model from typical software testing. We will discuss the challenges that came across, the Process involved in building an ML model. We take an example of Search Relevance for an explanation. We will dive into the areas where quality is assessed. The significant factors considered here are measuring Accuracy and Efficiency. We will look into the different black box testing techniques for different Algorithms. Let us also see how traditional testing is different from testing machine learning applications. I will go through different black-box testing techniques with examples following a live demo.

Puppeteer is a Node js library (developed by Google Chrome team) to control Chrome and Firefox and is getting lot of traction recently because of its amazing capabilities. It has already become so popular that it has got 50K+ stars on Github against Selenium's 15K+ stars.

In the last Google I/O event, this tool's capability has been showcased and it has been perceived as the next generation Web Test Automation Tool.

Is Puppeteer better than Selenium? The answer is 'No', and I will cover 'why' in detail during this talk. I will show some live examples to demonstrate that Selenium can also do all those advance things which Puppeteer promises to do.

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

'Testing is fun' - Michael Bodnarchuk, creator of CodeceptJS
I am pretty sure it is. But how many of us actually feel that way?
Set aside Testers with Development background and you will find testers struggling to get a good hold of different automation tools available in the market. I am no exception.

In the past five years, Automation is the buzz word in the Testing World. Fresh graduates or young professionals are absorbing the automation journey in their career path. But you will find very few Senior Testers or Lead Testers doing a good job at this new trend to succeed in the industry. They have either moved to managerial roles or on the Business side of the organization. While there is no harm in that but we are headed towards a time where we can not escape whatever automation brings to our industry.
Thus, let me introduce to you an Acceptance testing Framework which is - 'CodeceptJS'. It is an end-to-end NodeJS TestFramework.
You have the liberty to use any of the helpers -
1. Classic Selenium +WebriverIO
2. Protractor
3. Nightmare
4. Puppeteer
Few really useful features being -
-Interactive Debug
-Parallel Execution & multiple browsers
-Web & Mobile Testing
-Convert your scripts written in one of the helpers into another
-Scenario driver & cucumber-like BDD

In this talk, I will demonstrate how to get started with codeceptJS automated testing & take advantage of the userfriendly and multiple features to write, read and debug automation scripts.

Best Practices - How to get the best 'Return ON Investment' (ROI) from your Test Automation.

This has been observed that, most of the test frameworks wont be able to survive due to lack of expertise, no maintenance, no best practices being followed, and finally your test automation will be dead after few months, and there is no "Return ON Investment" from this. This is the most common problem, most of the companies are struggling and finally back to square to the Manual testing.

My proposal : HOW to leverage your test automation in terms of best practices, best ROI, and how to adopt best automation culture in your organisation.

I strongly propose some of the important points/suggestions to achieve this in your Organisation/Team.

1. Test Automation Practices:

• Design Patterns (Web/Mobile/API)
• What to Automate/Not to Automate

2. Common Automation Frameworks at Org Level:

• How to design Generic Utilities, Libraries and different Components, which can be suitable for all the teams in the same Org.
• Best practices to design your Tests (Automation).
  • Common Design Patterns
  • Common application level and Page libraries
  • Best Practices to use Assertions in your Tests (How and What to write for assertions). Most of the people don't write proper assertions and this is making your test unreliable and no defects found during execution.

3. Inclusion of API/Backend libraries in your UI test automation as an external Maven/Gradle Dependencies to avoid un-necessary tasks, some of the important points to be considered here:

• User Creation from APIs (No need to automate user creation from web/app for all the test cases)
• API tests are stabled most of the time
• API calls takes lesser time as compared to web, hence include API calls in your UI/App framework to save time.
• Less flaky test

4. Best Code Review Process (Do not merge your code into Master without proper Code Review)

• Implement PR (PULL Request) Process
• Static Code Analysis using SonarQube, Cobertura, JACOCO etc..
• Get the benefits of Best Test Automation Quality Matrices
• Sometimes, Manual (Functional Tester) should review your code (Assertion, test steps and use cases) to get the best coverage

5. Quality is A Team responsibility:

• Developers, POs, Manual QEs and Automation engineers should be included to get an overview of test automation coverage.

6. Maintenance of the Frameworks

• After couple of months, it should not make your life miserable if you don't maintain your libraries and framework properly.
• Do not use Hard Coded values, make it simple and Generic.

7. Infrastructure Setup for Test Design and Test Execution:

• Proper Browser - OS lab setup
• Proper Mobile Labs setup with different Devices - IOT, iOS, Android, iPad, Tablets
• Proper CI - CD common configuration using Jenkins, Dev Ops, AWS, Docker and Cloud setup
• Handling multiple Docker nodes using Kubernates (use of Selenoid, GRID on Cloud)

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 .

When it comes to creating a project we always search for ideas that are open-source, easily scalable, ready to use.

We as humans always have a tendency to grow and expand and when it comes to customizable infrastructure it's a big Yes.

In this talk, we will be focussing on how we can save the cost of actual servers with easily scalable and manageable open-source software using an open-source tool - selenium grid. How we set up the nodes and hub altogether to make cross-browser, multi-platform execution takes place.

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

It is always challenging for us while dealing with Xpath and Css in iOS Mobile Automation.Even we can't think to do iOS native app mobile automation without them .

So problems we need to face are

• High Maintenance
• High Execution Time
• Complex installation
• Additional Web server

Do we have some solutions for this problem - The answer is Yes !!! -> KIF(Keep It Functional)

Automation is now preferred the most, precisely in the agile environment as it saves a lot of time. But wouldn’t it be a privilege to save some few more time by locating the web elements? Writing automation script swill reduces the time in terms of regression testing, but generating the XPath and even an entire code statement will surely add a lot to the automation scripting.

My personal opinion about automation is that automation isn’t pure automation if most of the works are done manually with a hectic routine and having a high chance of erroneous XPath. Even the task itself will kill tonnes of time, right-click – inspect the element and writing the XPath in the DOM would take a lot of time.

As an automation tester, finding the locators and XPath are vital for test automation and one of the most used. But doing this iteratively for an entire day wouldn’t be interesting, Ruto does this sleek, not within seconds but in milliseconds. Opening the DOM and typing the XPath is time-consuming but writing the entire statement with few clicks will save a lot of time.

Ruto is free extension that is used to find XPath for test automation, the tool makes finding elements easy in the DOM along with that it provides code snippets based on the action that need to be perform on that element. To store the found element Ruto automatically suggests a variable name from the user’s perspective along with its return type. The user will be informed if the element is located inside a frame/iframe, Ruto also provides a whole set of elements from a web page as snippets. Apart from this, we’ve added one additional feature (experimental) to support Page Object Model.

XPath supported by Ruto:

• Supports dynamic XPath
• Axes based XPath can be achieved with Ruto by adding the web elements as source and target
• Ruto can be solely used to handle web table
• Provide multiple XPath at a time for an element

Did you know that Tesla can deploy firmware updates to its Model S seconds after a developer updates a module to improve the car’s acceleration performance or refine the car’s console?

Streamlined development-to-deployment efficiency arises elegantly from a smart automated testing strategy. In a world where time is precious spending hours to execute the tests is an

outdated approach and hence it is necessary to fill the gap using the latest technology. Our idea is to build an infrastructure that supports faster execution of the automated tests.

We plan to set up a selenium hub and selenium nodes in docker containers and once the nodes are registered to the hub ,initiate the process of test execution. This will parallelize the

tests and establish 1:1 mapping between the tests and containers .

Basic flow:

1.Automation framework will count the tests to be executed.

2.Set up the infrastructure of hub and nodes containers based on the count.

3.Check if the hub is ready to accept request.

4.Parallelize tests and send them to the hub for execution.

5.After completion of the execution kill the containers.

Benefits:

1.Reduce Execution time significantly.

2.Disposable runtime environments.

3.Enhance development and release cycle.

4.Lets new features and improvements reach the customer faster.

5.Save time and money of the company in long run.

6. No requirement for any reserved infrastructure

7. Cloud native and portable

Most of the time, Non-Functional reports are generated from different teams at the last moment of the release and so are chances of last-minute delays occur. So, how about keeping a track on NFT reports during the test automation execution.