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The state of software development is evolving rapidly as software developers and testers have started moving to DevOps. In the beginning, organizations used to have a simple process for development and operations, but now users demand a more enhanced and improved experience with every product, which requires proper testing, security, data, AI, and more.
Let's be honest: if you're using legacy test approaches, you spend a ton of time maintaining your data-driven tests. And that time slows you down when you're trying to keep up with a dev team that thinks, "We're coding to standards — it should all run everywhere."
Think about the most difficult parts of coding and maintaining your test infrastructure. The simplest part involves writing the initial tests. You use what you see and your understanding of expected behavior to drive the tests. Test maintenance costs can drive you crazy.
The purpose of DevOps is to bring development and operations together. It allows them to build, test and deploy applications while reducing time-to-market considerably. Increasing the convenience and popularity of DevOps have sparked the conversation that Quality Assurance has become an outdated concept. However, it is not true.
QA was an integral part of the waterfall development process but it still holds a relevant position in DevOps. The process of DevOps never allows a lot of time for comprehensive test creation between coding and release. This also means that teams are continuously on their toes to specify, build, test, and deploy software effectively and rapidly.
Many of us over the years have executed test automation against a production-like test environments. By the term "production-like," I mean test environments that have the same set up as a production environment but may or may not have the exact configuration as that of the production environment.
However, when it comes to executing test automation against the test environments, there is always a certain degree of challenge (although solvable) that an engineer faces. Classic examples include:
"I am confident that once you give this functional test automation approach a try, you will rethink your entire current code-based approach." — Raja Rao, Head of Test Automation University
In this webinar, you'll see the modern, intelligent way of doing web and mobile testing. Specifically, functional, end-to-end UI testing.
The analogy is a gasoline car versus an electric car: both are cars, both need tires, seats, breaks, etc... but the core engine that moves the car is different - which makes a huge difference.
In this article, I want to give you an overview of how well the latest technologies can be integrated seamlessly into a test automation framework. Test automation is also an integral part of the software development lifecycle, and we want our test automation framework to contain all the latest features with minimal effect.
In this article, we build a sample test automation framework that consists of tools like Spring Boot, Cucumber, Java 8, and Serenity to test a sample Calculator application.
In traditional software development, testing professionals manually tested the developed software but the need to test redundant scenarios necessitated the use of testing tools that would allow them to execute the same tasks automatically.
The benefit of automated testing was a reduced time to test legacy test scenarios, while the downside was that test automation involved the use of scripts. Testers needed to either learn the supported languages for automation testing or hire new resources who knew to code.
If you have worked in the technology industry during the last few decades, you have heard the term "Agile" more times than you can count.
Agile, DevOps–the industry tosses these buzzwords around like free candy. But how can companies become agile in today’s technology sector?
Pytest is a test automation framework that lets you write simple to complex functional tests. This tutorial will walk you through the great features of Pytest, including:
You may also like: Improve Your Selenium WebDriver Tests With Pytest
To install Pytest, run the following command:
I can relate to the feeling where you wish to achieve flawless automation testing in the blink of an eye! On the other hand, I also understand the apprehension that is causing you to delay test automation. When an enterprise has just begun the transformation, there are a plethora of challenges to overcome. Even if you apply the best practices, some shortcomings can result in wastage of automation efforts. So, I’ve decided to list out 22 reasons that in my experience, are responsible for automation testing failure.
At one point in time, server-side code was responsible for generating and serving every page in our application. Nowadays, Single Page Apps (SPA) are taking a bigger slice of the pie, as more clients are leaning towards building their applications as an SPA.
Developers spend most of their time debugging SPAs inside the browser by debugging the JavaScript files, CSS styles, and the DOM.
Load Testing can monitor the response times of the system for every single transaction during a set period of time. This kind of monitoring can bring significant information, precisely for stakeholders and business managers, who look for conclusions based on these outcomes, along with any data for supporting these findings. Such type of testing can also raise attention to any complexity and issues in the application software and fix these performance bottlenecks before they become extra problematic. As a developer or QA engineer, one can possibly incorporate several kinds of tests into the code checking: integration tests, unit tests, UI tests, and more. Load testing supports testers or engineers to understand the behavior and accessibility of the application. It critically tackles the following queries:
• Do the codes show the desired performance?
Nowadays, more and more companies are building test automation frameworks based on WebDriver and Appium for testing their web and mobile projects. A big part of why there are so many flaky tests is that we don't treat our tests as production code. Moreover, we don't treat our framework as a product. I will present to you all the challenges during the testing of our test automation framework. In the first article from the series, I gave you an overview of our test infrastructure like what tools we use for source control, packages storage, CI and test results visualization. In this second part, we will discuss various types of testing that we did to verify that each aspect of our framework is working as expected — functional, compatibility, integration, usability, installability and many more.
Before I share the details of our test infrastructure, I need to explain what we have to test. Our test automation framework is written on .NET Core in C#. This way we achieve the cross-platform requirement. It also has modules for API, desktop, web, Android, iOS and load testing. Generally, there are two ways to use the framework. Most of our clients use it by installing NuGet packages. (NuGet is the package manager for .NET. NuGet client tools provide the ability to produce and consume packages.) To ease the process of configuring the projects we provide a Windows UI installer and CLI installer for Windows and OSX. The UI installer also adds various projects, item templates, and snippets for better user experience in Visual Studio IDE.
We are in an era of automation everywhere! Some time back, Bob Egan, the Chief Research Officer at Sepharim Research, talked about mobile security. He made a statement on Enterprise Mobility Trends 2016:
“The modern desktop is really not a desktop, but an experience that fits the moment.”
He also added saying that we are getting into a generation where there will be applications developed specifically for the work to be done easily and efficiently. I totally agree with that and believe that we are highly dependent on minimizing our work efforts with the help of various tools.
JavaScript? It's a standard. Selenium? It's this great WebDriver. So, why do I get visual errors?
In this article, I go back to basics. I demonstrate how to test a UI for native JavaScript applications using a combination of two tools:
KMS’s Vice President of testing services, Mush Honda was recently a guest speaker on Test Talks. The podcast is hosted by automation expert Joe Colantonio and covers all things software quality and automation.
In this episode, Mush talks about how testing is evolving in tandem with the industry.
Model-Based Testing (MBT) itself is not new, but Model-Based Test Automation is experiencing a resurgence in adoption. Model-Based Testing is the automation technique with the greatest current business interest according to the 2018 World Quality Report, with 61% of respondents stating that they can foresee their organization adopting it in the coming year. [1]
Technologies like The VIP Test Modeler have significantly reduced the time and technical knowledge needed to model complex systems. Organizations can now enjoy all the benefits of Model-Based techniques within short iterations, whereas previously modeling had been reserved for only the most high-stake projects, such as lengthy Waterfall projects in aerospace and defense.
Organizations today have long understood the need to automate test execution, and 90% believe that automated testing allows testers to perform their tests quicker. Yet, QA teams are struggling to achieve sufficiently high rates of automated test execution. Slow and overly manual testing still abounds.
In 2018, 61% of organizations had automation rates lower than 50%. This article considers five reasons for these low rates of functional test automation, setting out some of the most common pitfalls to watch out for when adopting a test automation strategy.