Test automation automates the creation, execution, and analysis of test cases. Effective automation helps teams test and develop quality features while creating time to implement new feature requests.
Executives know that manual testing slows down development and impacts important business metrics, such as churn and time-to-market. Also, developers know that manual testing is a tedious and time-consuming process that yields low-quality test implications.
In this post, you’ll learn how test automation benefits not only developers by streamlining software development, but also the business as a whole by influencing valuable metrics such as churn, time-to-market, revenue, and more. If you’re looking for an overview of test automation, I encourage you to read the benefits and use cases of test automation.
Automatic Test Generation
Generating test cases is a crucial aspect of test automation as it executes existing tests and creates new ones efficiently.
Automatic test generation saves developers time and lets them create more advanced tests than humans have the time or ability to. It comes in many forms with tools like Speedscale for testing non-functional aspects—like resiliency and scalability—of applications.
As of writing, it wasn’t long ago that a single engineer brought down Twitter because of a “bad configuration change,” which perfectly shows why it’s important to automatically generate tests. If their deployment pipeline had included a simple regression test using production traffic replication—replaying production traffic in a development environment—they would’ve verified the functionality of all common use cases of their API and discovered the faulty configuration before deploying it to the public.
The Twitter example above is one of the various forms of automatic test generation. Irrespective of the methods, the goals of automatic test generation stay the same: boost developer productivity, application stability, and performance.
Boost Developer Productivity
It’s not hard to see how test automation increases developer productivity; developers won’t spend time creating tests. But it goes beyond that. Automated tests cover more use cases and code. A single API endpoint can be tested in several ways:
- Positive – Valid request and expected response
- Negative – Invalid request expected error response
- Boundary – Test request with values at the boundaries of valid ranges
- Missing parameters – Validate error handling of missing information
- Extra parameter – Validate error handling of malformed request
And the list goes on… All the tests can be run for a single endpoint with a single behavior. Now think of the numerous endpoints you have and the different behaviors expected from each endpoint. Does the response change based on the user’s authorization? Does it change based on parameters like
?sort=name? Can you change the behavior with certain parameters like
?mode=compact? This is before considering how it reacts to different request bodies.
Hopefully, it’s clear that it’s unlikely to test all possible API scenarios. Although test automation doesn’t guarantee you’ll test 100% of your code, it comes much closer than manual testing. As a result, developers will spend less time “fighting fires” and troubleshooting because they’ll be notified of bugs earlier in the development process and fix them before deployment.
Even if you only use test automation to replace existing test execution, it’ll likely increase efficiency as computers can do any task faster than humans.
Increase Application Stability
Increased application stability is the result of everything mentioned in the previous section. Unsurprisingly, catching more bugs early in the development process reduces the amount that reaches production, increasing stability.
However, increasing stability isn’t just about catching bugs in new code. It’s also about performing regression testing to ensure the new code hasn’t impacted the existing functionality. Like the Twitter example mentioned earlier.
Regression testing isn’t new, and many software teams have implemented it over the years. But, like the API example in the previous section, test automation lets you perform regression testing on almost every part of your application—an impossible feat with manual testing.
Again, a more stable application means fewer engineering hours spent troubleshooting and more time spent on improvements and new features. In other words, a streamlined development process.
Developers will have more time to add new features or improve existing ones as the development process streamlines and fewer bugs get into production.
In my experience, we developers enjoy coding because it encourages problem-solving. Some developers are more interested in solving the problems associated with implementing new functionalities, while others prefer cracking the code on new optimizations.
Test automation allows the group interested in optimizations to analyze test results, try out new optimization techniques, and ultimately improve the application’s performance. For example, to determine the application using the most resources, generate consistent and automatic traffic toward an application running in a preview environment with mocked dependencies.
Choosing the Right Tool
Choosing the right tool is crucial for success no matter the situation; it’s true for test automation too. Good tools are simple to install, use, and maintain. Overall, it should provide a good developer experience and powerful functionality.
But what functionality should you look for? Apart from generating and executing tests, it’s vital—especially for test automation—that the tools can integrate with other parts of your development process.
For example, if your test automation tool can’t integrate with your CI/CD provider, you won’t enjoy the full benefits of test automation. Yes, developers benefit from automatic test generations because it lets them test larger parts of their applications. But the real power comes from integrating test execution into your workflow and validating functionality before merging a pull request.
There are tools that cover the use cases of most development teams, but you also need another tool at times. For example, Speedscale allows you to replay traffic within a Kubernetes cluster. But to send a single API request, you may want to use Postman. Although Postman lets you view necessary information, like request headers and parameters, manually adding the information to Postman is inefficient. Instead, proper integration allows you to export a Postman collection and import it into Postman, which ensures zero mistakes when transferring the request from Speedscale to Postman.
In some cases, some developers prefer the interface of K6 because in which case you can export recorded traffic and use it to create K6 tests. Or perhaps you’ve already got monitoring set up in Datadog, and you like the monitoring and reporting abilities it gives you. In that case, your tool should be able to export data to Datadog.
The point is that generating and executing tests isn’t enough, especially in a modern infrastructure like Kubernetes. Rather, integrating with other tools that enable flexibility is crucial for streamlining the development process.
The Business Benefits of Streamlined Development
Fewer bugs, more test coverage, and more time to spend on new features are music to the ears of developers. But many developers work at for-profit companies that are reluctant to implement changes that don’t improve business metrics. Luckily, business owners also benefit from a streamlined development process.
Quick release of reliable features is a major benefit of a streamlined development process. It also helps organizations react faster to market changes or get customer feedback earlier, leading to lower up-front and opportunity costs.
When you thoroughly test applications, fewer bugs enter production, which improves the user experience. This is why organizations that prioritize customer satisfaction should implement test automation.
Test automation cut costs in three main ways; infrastructure savings, engineering hours, and opportunity costs.
Because test automation allows for the creation of preview environments, it reduces the resources you need to keep running 24/7. Also, test automation can aid you in creating powerful comparisons.
It isn’t an inherent feature of test automation, but it’s possible depending on how you implement it. For example, traffic replay can consistently generate traffic to determine the optimal Kubernetes cluster configurations.
Your reputation will improve as the stability of your application increases, and you develop new features that satisfy customers. It’ll get even better if you respond to customer feedback quickly.
And word-of-mouth will always be one of the best marketing tactics.
Increased capacity for growth
As business metrics and operations improve, your product needs to grow too, especially if you’re a SaaS platform. But unprepared growth can invalidate the benefits of test automation.
Perhaps you implemented autoscaling rules because you expected a certain growth level. But your application will become unstable if you don’t factor in the time for spinning new instances during scaling.
In the best-case scenario, users will understand the situation and return later. In the worst-case scenario, users will lose interest in your product because of a negative first impression. Implement automated tests for autoscaling if you’re expecting growth.
Increased revenue and likely increased profits
Collectively, all the above benefits cause revenue increase for businesses, which is the most important advantage. Your revenue will very likely grow if you have happy customers, reliable features, and high innovation potential.
Whether this increases your profits depends on internal factors that it’s impossible to blatantly state increased profits as a benefit. But increased profits are likely considering how streamlined development saves engineers’ time.
The Importance of Integration
There are two key takeaways from this post—test automation can improve business metrics, and good test automation requires integration.
The last section showed how to improve business metrics, and to reiterate previous sections, good integration paves the way for other advanced concepts like continuous performance testing. Although having tests automatically generated and available to be executed at any time, the optimal approach is to integrate them in the development process itself.
This isn’t a new concept by any means, organizations have been running unit tests—or even integration and end-to-end tests—as part of CI/CD pipelines for years. The key difference with test automation is its testing efficiency and the possibilities it provides in terms of non-functional testing.