Long time no see? Where have I been

It has been a few months since I published anything on my blog. Does not mean I have not been learning or experimenting with new ideas. In fact, in the past few months I have been privileged to have my articles published on Applitools and Test Project blogs.

Below is the link to all those articles, for which I have received very kind reviews and comments on LinkedIn and Twitter.

Apart from this, I have also been contributing to open source - namely - Selenium, AppiumTestDistribution and building an open-source kickstarter project for API testing using Karate and for end-2-end testing for Android, iOS, Windows, Mac & Web.

Lastly, I have also been speaking in virtual conferences, webinars and last week I also recorded a podcast, which will be available soon.

The end of Smoke, Sanity and Regression

https://applitools.com/blog/end-smoke-sanity-regression/

Do we need Smoke, Sanity, Regression suites?

• Do not blindly start with classifying your tests in different categories. Challenge yourself to do better!
• Have a Test Automation strategy and know your test automation framework objective & criteria (“Test Automation in the World of AI & ML” highlights various criteria to be considered to build a good Test Automation Framework)
• Choose the toolset wisely
• After all the correct (subjective) approaches taken, if your test execution (in a single browser) is still taking more than say, 10 min for execution, then you can run your tests in parallel, and subsequently, split the test suite into smaller suites which can give you progressive indication of quality
• Applitools with its AI-power algorithms can make your functional tests lean, simple, robust and includes UI / UX validation
• Applitools Ultrafast Grid will remove the need for Cross-Browser testing, and instead with a single test execution run, validate functionality & UI / Visual rendering for all supported Browsers & Viewports

Design Patterns in Test Automation

https://blog.testproject.io/2020/06/29/design-patterns-in-test-automation/
 

Criteria for building a Test Automation Framework

Writing code is easy, but writing good code is not as easy. Here are the reasons why I say this:

• “Good” is subjective.
• “Good” depends on the context & overall objective.

Similarly, implementing automated test cases is easy (as seen from the getting started example shared earlier). However, scaling this up to be able to implement and run a huge number of tests quickly and efficiently, against an evolving product is not easy!

I refer to a few principles when building a Test Automation Framework. They are:

• Based on the context & (current + upcoming) functionality of your product-under-test, define the overall objective of Automation Testing.
• Based on the objective defined above, determine the criteria and requirements from your Test Automation Framework. Refer to my post on “Test Automation in the World of AI & ML” for details on various aspects you need to consider to build a robust Test Automation Framework. Also, you might find these articles interesting to learn how to select the best tool for your requirements:
• Criteria for Selecting the Right Functional Testing Tools
• How to Select the Best Tool – Research Process
• How To Select The Right Test Automation Tool

Stop the Retries in Tests & Reruns of Failing Tests

https://applitools.com/blog/uncover-flaky-tests/
 

Takeaways

• Recognise reasons why tests could be flaky / intermittent
• Critique band-aid approach to fixing flakiness in tests
• Discuss techniques to identify reasons for test flakiness
• Fix the root-cause, not the symptoms to make your tests stable, robust and scalable!

Measuring Code Coverage from API Workflow & Functional UI Tests

https://blog.testproject.io/2020/11/24/measuring-code-coverage-from-api-workflow-functional-ui-tests/

 
Why is the Functional Coverage important?

I choose an approach keeping the 80-20 rule in mind. The information the report provides should be sufficient to understand the current state, and take decisions on “what’s next”. For areas that need additional clarity, I can then talk with the team, explore the code to get to the next level of details. This makes it a very collaborative way of working, and joint-ownership of quality! 🚀

You can choose your own way to implement Functional Coverage – based on your context of team, skills, capability, tech-stack, etc.

Does your functional automation really add value?

We all know that automation is one of the key enablers for those on the CI-CD journey.

Most teams are:

• implementing automation
• talking about its benefits
• up-skilling themselves
• talking about tooling
• etc.

However, many a times I feel we are blinded because of the theoretical value test automation provides, or because everyone says it adds value, or because of the shiny tools / tech-stacks we get to use , or ...

To try and understand more about this, can you answer the below questions?

In your experience, or in your current project:

1. Does your functional automation really add value?
2. What makes you say it does / or does not?
3. How long does it take for tests to run and generate reports?
4. In most cases, the product-under-test is available on multiple platforms – ex: Android & iOS Native, and on Web. In such cases, for the same scenario that needs to be automated, is the test implemented once for all platforms, or once per platform?
5. How easy is it to debug and get to the root cause of failures?
6. How long does it take to update an existing test?
7. How long does it take to add a new test?
8. Do your tests run automatically via CI on a new build, or do you need to “trigger” the same?
9. What is the test passing percentage?
10. Do you “rerun” the failing tests to see if this was an intermittent issue?
11. Is there control on the level of parallel execution and switch to sequential execution based on context?
12. How clean & DRY is the code?

In my experience, unfortunately most of the functional automation that is built is:
· not optimal
· not fit-for-purpose
· does not run fast enough
· gives inconsistent feedback, hence unreliable

Hence, for the amount of effort invested in implementing automation,

1. Are you really getting the value from this activity?
2. How can automation truly provide value for teams?
   

Tracking functional coverage from your api / functional UI (e2e) tests

Tracking and having a high coverage of your product code via automated tests is an important way of building a quality product.

It is easy to measure code coverage when running unit tests. You will find a plethora of tools (free & commercial) with a variety of features for any programming language you may be using. You can integrate these tools as part of your pre-commit hooks (i.e. you will not be able to push your code to version control if the limits set for code coverage fall below the minimum limit set), and also as part of your CI builds (i.e. fail the build if the code coverage limit falls below the expected limit).

The reason capturing code coverage works easily for unit tests, and maybe integration tests, is that these tests run in isolation, directly on the code. You do not need to have the product deployed to any environment to run the tests to measure the coverage. I found these great resources you can check out to understand more about how code coverage works:
http://www.semdesigns.com/Company/Publications/TestCoverage.pdf
https://confluence.atlassian.com/clover/about-code-coverage-71599496.html

However, very frequently this question comes up - how can we measure code coverage of the API tests, or of the functional UI (e2e / end-2-end) tests? I remember this question coming up since the past 8-10 years at least. Every time, I have given the same answer because I have not come across, nor seen any better way of answering this question. It is time I wrote it down for easier access to others as well.

Solution #1

Preconditions:

A big criteria for the above strategy to work is to ensure the environment is isolated - i.e. NO ONE is using the environment (for navigating through the product, testing of any kind other than the tests being triggered to measure coverage).

• Deploy the product-under-test to an isolated environment, and start measuring code coverage. 
• Then trigger the API / UI tests, 
• That will tell you how much code coverage is achieved by these tests.

The above answer has some big gaps though:

• What are you trying to understand from the code coverage of your API / UI tests? What value will it bring to the team? How will it make the product better?
• Do you expect the code coverage of your API / UI tests to be similar / identical / better than the unit tests? IF yes, we need to have a different conversation about the Test Pyramid

 

Solution #2

However, I believe there is a better approach to this. 

Based on the Test Automation Pyramid shown above, the API / Web Service tests and UI tests are business facing tests. In this case, it will add more value to measure what functional / component coverage the tests have. 

With this approach, the code coverage from the Technology Facing Tests (Unit / Integration / ...) will focus on technical aspects of coverage, and the Business Facing Tests (API / UI) will focus on functional aspects of coverage. 

When looked at together, this will give a better sense of understanding of the overall quality of the product.

Tracking Functional Coverage

So, how can we track functional coverage? 

Unfortunately, there does not seem to be an out-of-the-box way to do this. But below is how I have implemented this before:

• I used cucumber-jvm in my test framework
• For each (end-2-end Test) Scenario, in addition to the tags required for the test (as per the test framework design), I added the following types of tags to the test:
• functional areas touched by the test
• components / modules touched by the test
• I used cucumber-reporting plugin to generate rich, html reports
• The beauty of the reports generated by cucumber-reporting reports is that I can now see for my test execution the different statistics of the tags when the tests ran. 
• Below is an example for the cucumber-reporting github page:

 

• With this report, you can now get to know:
• the overall tag execution state for the complete test run
• the number of times the tag was run as part of how many test scenarios
• drill down into tag-specific reports as well

Why is the above report important?

As mentioned above, I am adding custom tags to each scenario - based on module / functionality / etc. If there is any critical functionality of my product, I would want to have more concentration of tests covering that feature / module, compared to others. 

Another way to visualize the tag statistics is in form of tag heat maps. You want your critical functionality to have a good sized bubble in the heat map. Also, any small bubbles, or non-existing bubbles would mean you do not have coverage for that feature / module.

The above example is one of the easiest ways to implement feature coverage for API / UI tests. But it is very likely you are not using cucumber-jvm, and cucumber-reporting plugin. But if this approach makes sense to you, then you could very easily implement it in your test framework, using the constructs and features of that programming language and tools.