Why You Should Consider the Testing Pyramid Structure?

The purpose of the test pyramid is to provide a structure for designing and executing tests. It is also the cornerstone of testing at Google.

The test pyramid has four layers – unit tests, service tests, integration tests, end-to-end or acceptance tests. By dividing up the different types of tests into these levels, it becomes easier to decide what type of testing needs to be done when building and maintaining software.

At the bottom are unit tests. Unit tests are “white box” tests because they interact directly with product code. They typically perform operations on functions, methods, and classes. Unit tests should be concise, to the point, and focused on a single object/variation. They should be devoid of external dependencies and should instead rely on mocks/monkey-patching. These tests took less time to execute.

Integration tests are located in the center. Integration tests examine the point at which two distinct entities come together. They should be “black box” in nature, interacting with actual instances of the product being tested, rather than with code. Integration tests include service call tests (REST, SOAP, and others).

End-to-end tests are at top level. End-to-end tests trace a system’s path. They could be argued to be a multi-step integration test, and they should also be “black box.” Typically, they interact with the product in the same way that a real user would. Web UI tests are an example of integration tests, as they require the entire stack to run.

And manual testing is the cherry on top. Certain types of tests, such as exploratory or usability testing, cannot be automated, but we should always strive to minimize the number of manual tests.

You should be aware that as we progress up the pyramid, three factors begin to increase:

  • Costs associated with the development and maintenance of tests
  • Time frame for execution
  • Possibility of erroneous negatives

Key aspects relating to the software testing pyramid include the following:

Avoid conducting duplicate tests at multiple levels. There is no reason to conduct the same tests at various levels. For instance, if boundary values were validated in unit tests, they should not be repeated in the GUI – you will not obtain any new data.

One should strive to reduce the difficulty of tests whenever possible. For example, you can verify interest calculation on a negative sum at a “medium level” or even at the unit test level. Therefore, why should you do it at the UI level? Lower-level test automation is more efficient; it enables you to identify defects earlier and saves you time and money.

Optimize Selenium WebDriver Automated Test Scripts: Maintainability

[A must read for all SDETs and Automation Engineers].

This time Zhimin Zhan provides a number of code examples for improving test maintainability.

When you can run automated tests often, and the application changes all the time, it is important that test scripts are: Quick and easy to maintain. Easy to read.

The leading reason for test automation to fail is that the team fails to maintain its automated scripts. In this article, Zhimin Zhan helps to understand some simple and practical tips at the test steps level that can help you make your automated functional tests more maintainable. These techniques are:

  • One Test Script Line for One User Operation
  • Default Parameter Value
  • Extend function behaviour with optional Hashmap

Click here to Read complete article.

Testing in Production at LinkedIn

If you can’t confidently deploy to production, write tests until you can! Learn about all of LinkedIn’s testing processes for releasing features to its 500 million members with confidence.

The company’s primary app, linkedin.com, receives around 1000 commits every week from over 300 different contributors. We have other products that span 5000 codebases in addition to flagship. Let’s take a closer look at: – in-production testing – feature flags and canaries – microservices testing – putting machine learning models to the test – putting search and relevancy algorithms to the test – engineering for resilience This session will assist you in developing a product testing plan. Continue to write excellent tests!

What is Robotic Process Automation (RPA)? Benefits? Explained

Face it, we’re all destined to be replaced by machine. Robotic Process Automation (RPA) is a type of software that uses artificial intelligence (AI) and machine learning. It handles high-volume, repeatable tasks previously done by humans. This type of software includes queries and calculations.
What Can RPA Do? RPA applies universally to any type of business; it can be used for example in IT infrastructure, services and procurement. The use cases are unlimited and span across all industries. An example use case is shown below.

Image Source: https://www.teplar.com/blog/importance-of-robotic-process-automation-rpa/

The importance of RPA for process automation:

  • it can improve speed, quality and productivity. It can replace un-intelligent tasks that take up a lot of time with faster and more accurate activity.
  • RPA will free your employees up to perform more crucial tasks for your business, as it can take out all the mundane and repetitive jobs that they would typically perform. That way, they can move on to more important work and potentially even upskill in the future.
  • RPA can help your organization save time and money, and prepare you for competition.
  • Get more out of the data you have: RPA is ideally suited to help parse through large datasets, both structured and unstructured, helping organizations make sense of the data they are collecting.
    We tend to produce so much data that it can be hard to process it all. There is a great opportunity for you to get more out of the data you have – learn what your insights are and see how this can drive efficiency throughout the company.