When teams choose to introduce automated testing tools into their software development life cycles, they speed up testing cycles, improve overall testing accuracy, and produce better product.
Automated tools for software testing have come in three waves: standard craft tools, at-scale test automation tools, and the newest intelligent testing tools.
Looking to compare tools? Checkout the pros and cons summaries for the top automated tools for software testing.
You want to add more tests to your software development life cycle, but where do you begin? Most teams acknowledge that testing is foundational to modern-day development practices — think Continuous Integration, Continuous Delivery/Continuous Deployment, and DevOps. But, running consistent and reliable tests with actionable results can be challenging in practice.
Organizations can find testing too labor-intensive, time-consuming, and overall difficult to run all of the best possible tests at every stage of development. Many businesses are turning to automated testing tools and using them to run tests automatically on a cadence rather than relying on human intervention.
Just as with other areas of software development, automation enables teams to streamline and speed up their testing processes. Automated tools ultimately make it possible for teams to test their software daily.
When teams choose to introduce automated testing tools into their software development life cycles, they see improvements across their entire development process. A few of the biggest benefits from using automated tools for testing software include:
Faster testing cycles. Automated tests take a fraction of the time that it takes manual tests to run. As with any automation tooling, it also reduces the chance of bottlenecks because it requires less human-driven checkpoints. Instead of waiting on a person to manually execute a test, these automation tools simply take the code or software in question and run it immediately.
Reduced human error & improved overall accuracy. Software testing is often very repetitive. And if a human has to perform these types of menial, repeating tasks, then there’s a good chance that they’ll let errors slip by. Instead, automated testing tools perform repetitive tests with a consistent level of accuracy.
Long-term cost savings. Although test automation has a high upfront cost, it provides a high return on investment overtime because it requires less personnel, boosts developer productivity, and produces faster results than manual tests.
Better tests make a better product. Nothing is perfect, but you can still strive for perfection. With good automated tests, your devs will catch more bugs before they hit production. That is a great selling point on its own, but it’s more than that. Fewer bugs overall mean your application is more stable, which makes users happier.
If any of your DevOps engineers or developers have been in the industry for several years, they’ve probably seen huge changes within the automated testing tool world. There have been two waves of automation testing tools in the past two decades, and we are emerging into the third wave of test automation technology. Depending on when it was released and which function it performs, each type of test automation tool fits into one of these three waves.
Test automation tools had to start somewhere. Their beginnings were in craft tools, intended to automate very specific test functions. As an example, Selenium was created to pose as a user on a web browser and automatically check functionality. JUnit is another example, as it focuses on providing unit test frameworks.
These tools are more manual in approach than tools within the other two waves. They rely on human expertise to kick off the tests then analyze the results in context.
At-scale test automation takes a more holistic approach to running test suites. Rather than just automating each on-off test and expecting humans to “press the start button” on these individual tests, at-scale test automation runs the entire test suite automatically, with a focus on building out a test routine and enabling repeatability.
Well-known CI/CD pipeline software, such as Jenkins and GitHub, enable this type of end-to-end test automation. Unlike wave one, which required testers to kick off each type of test, these tools run multiple tests based on pre-set workflows or other criteria.
Even though automated testing tools enable repetitive, scalable tests, they still have downsides. This is why a new wave of smart tools are appearing within the test automation world.
Wave three is the next generation of automated software testing tools. This evolution of test automation is all about data-driven, intelligent automation. Technology like machine learning brings new contextual awareness into these processes.
For instance, wave two would approach a test suite by running the same tests for every situation, or relying on a human to cherry-pick the correct one. Wave three smart tools, on the other hand, automatically take a deeper look into each specific case and its nuances, then run tests according to these factors.
The best testing practices for today’s world contain all three waves of test automation. Wave one testing tools provide expert support for running specific, specialized tasks, and can be implemented alongside other automation techniques.
Wave two testing tools integrate test automation into the bigger picture of developing with a CI/CD pipeline and DevOps techniques. Wave three testing tools fill many of the gaps left behind by its predecessors by intelligently analyzing test suites, rather than just blindly executing them. Here are a few examples of tools from all three waves:
Selenium automatically drives a browser, mimicking how a real user would interact with it. While powerful and beneficial, Selenium tests take a long time to execute, and running them too often will end up costing unnecessary resources and time.
JUnit is an open source framework, specifically created for performing Java unit tests. While they are helpful and can fit into several types of test suites and test cases, each individual JUnit test may not be applicable to every situation, meaning that testers often have to manually choose which ones are the best fit.
The Cucumber platform is known as a Behavior-Driven Development (BDD) tool. This means that it generally enables developers to collaborate on documentation and executable specifications. When it comes to testing, many developers use Cucumber to run custom-built tests. Although helpful, tests built out of Cucumber’s executable specifications are tailored to specific situations and not as versatile as other options.
Eggplant provides automation for testing user experience, specifically. It focuses on several aspects of testing, from generating test cases, to executing the right tests, to analyzing results. While a great choice for testing user interfaces, Eggplant can only be applied towards the end of the software development lifecycle, and needs to be supplemented with more shift-left testing tools.
This product suite offers testing across the software development lifecycle, easily integrating into CI/CD pipelines and broadly making comprehensive testing tools available to teams in a single platform. While Saucelabs has the capability to run several different types of tests throughout the SDLC, this process can take a long time, leaving teams with the option of either running parallel tests (which takes up a LOT of resources!) or adding in other tools that can narrow down the number of tests being run for each situation.
Mabl provides end-to-end tests for software teams to use throughout their processes. They focus on a data-driven, reliable approach to testing, with intelligent features such as auto-healing for tests. Because mabl focuses on a no-code approach, it does not allow users to add in their own custom-written tests, which can be a hindrance if custom tests are needed in specific situations.
This AI-powered test automation platform boils down and simplifies the entire process of running tests – writing, running, analyzing, reporting, and maintaining them. But because it is so feature-heavy, Applitools can be slow at times and requires teams to run parallel tests in order to perform at a good speed (which can be cost-intensive).
Our test intelligence platform, Launchable, was built to complement other test automation platforms by identifying and running the specific tests that are needed in each situation. Most teams have to choose between running an entire test suite a few tests at a time, which can take up a lot of unnecessary time, or opting for parallel testing, which can rack up high costs. Instead of having to take either of these paths, Launchable enables teams to pick the right tests for the right times, reducing idle time by as much as 70%.
|Selenium||One of the most popular testing frameworks, Selenium, is a collection of automated testing tools to test web applications and browsers. Thanks to its open-source nature, Selenium offers a wide swath of testing methods to fit the different needs of a developer team.||It’s free and open-source. Supports a wide range of browsers and languages.||Requires your devs to write tests manually, giving it a steep learning curve. It is only designed to work with web apps and browsers, not full applications. Selenium doesn’t provide any reporting methods to show the outcome of your tests.|
|JUnit||JUnit provides a complete unit testing platform for Java applications, stemming from the larger xUnit testing framework family. Over the years, it has become the de facto Java testing solution, almost edging out nearly all competition.||A fully robust unit testing framework for Java applications. Tests can be run as individual unit tests or test suites.||JUnit can’t handle large test suites. It can’t run dependency tests, unlike other testing frameworks.|
|Cucumber||Unlike other testing tools, Cucumber is a software tool designed around behavior-driven development, an alternative to test-driven development. Developers use Cucumber’s human-readable language called Gherkin to make tests easy to read and digest.||Tests are written in readable English, making them easy to write and understand. Covers a wide array of languages for testing.||Gherkin requires developers to write tests very precisely, which can be difficult. Not all teams will embrace behavior-driven development.|
|Eggplant||Eggplant offers a smart testing solution to help devs save money and time by leveraging AI with software testing. This tool works across any OS, application, or website to give your team a complete testing platform to cover it all. To do this, they use AI paired with computer vision to watch your users and learn from their actions to create tests.||Comprehensive coverage of testing solutions for every organization, including APIs and UI/UX. Computer vision paired with AI/ML tools allows devs to test all aspects of an application, down to minute details.||Computer vision can become challenging to use with significant UI changes in your application. Test speed is slow, as the software has several moving parts.|
|Saucelabs||Saucelabs offers a cloud-based testing platform that covers all aspects of an application. It integrates into your CI/CD pipeline easily and provides comprehensive reporting tools to ensure your developers know precisely what needs to be done. You can even run tests on various devices to ensure your mobile application is flawless.||It supports nearly every device, OS, and language available. Offers comprehensive, easy-to-read reports once testing is done.||The price is steep for smaller organizations. There’s a steep learning curve for using Saucelabs.|
|mabl||What if you could almost eliminate your dev team from writing code for tests? That’s the goal of mabl, a low-code software testing platform. Instead of writing tests, your QA team navigates through your application and shows mabl what to test for.||Provides testing with minimal code. It’s incredibly easy to integrate into your workflow and train your team.||Tests don’t run as fast as similar testing solutions. There are some UI elements that mabl can’t properly test.|
|Applitools||Another smart solution, Applitools, offers a powerful AI-driven testing platform for your application’s UI. Like Eggplant, Applitools uses visual inputs and AI to create and run tests, even comparing images and accounting for dynamic content. Their tests are faster, more stable, and catch more bugs compared to manual testing.||It dramatically reduces time spent writing manual tests, thanks to AI. They offer a sleek dashboard, so you get detailed reports of your test results.||Applitools is only for web and mobile app UI testing. It was created primarily for web testing, so mobile app testing isn’t as thorough.|
|Launchable||Launchable provides software development intelligence platforms that use machine learning to make testing data-driven. Our tool integrates with your CI pipeline and enables your team to use ML to only test code that’s been changed. Why force your team to run unnecessary tests when they can save time and ship confidently?||Save time by only running the tests you need to run. Keep your dev team happy by reducing the amount of overhead with other solutions.||As the AI model is trained across different tests, it can take some time to get data insights as the model learns. Launchable doesn’t help you write tests. Instead, it tells you what needs to be tested.|
Testing doesn’t need to be a long, time-consuming process. With Launchable, you can use machine learning to dramatically reduce the number of tests you run in your CI/CD pipeline. Every time your team makes a change, Launchable will rank your tests by importance. Then, it only runs the tests you need to!
Make your developers happy, don’t force them to run a full suite of tests for every change your team makes. Cut the noise and let your team do what they do best: innovate!