Software testing is an ongoing process that verifies a software application, ensuring it works as designed.
When a team uses testing throughout the software development lifecycle (SDLC), they prevent bugs, reduce costs and time, and create an overall better end product.
Let’s not sugar-coat it – a lot can go wrong when developing software. There are tons of moving parts that have the potential to break– artifacts, automation engines, tools, and so much more. Software developers are human, so it’s inevitable to make mistakes.
With all this said, how do modern development teams ensure their software products and apps perform well? The solution lies in software testing at every point of the development lifecycle.
Software testing is an ongoing process that verifies a software application, ensuring it works as designed. When a team uses testing throughout the software development lifecycle (SDLC), they prevent bugs, reduce costs and time, and create an overall better end product.
Many of today’s development frameworks are built on the concept of running test cycles early and often. Take the CI/CD pipeline as an example – Continuous Integration and Continuous Delivery/Continuous Deployment rely on testing to create a high-quality product.
DevOps methodology relies on streamlined software testing. Since DevOps is all about collaboration between business units, teams need definite ways to find and fix errors in a way that assigns clear accountability and responsibility. Frequent software testing does this by finding specific errors early in the cycle and pinpointing precisely what needs to be remediated.
Software testing techniques fall into a few categories: manual, automated, static, and dynamic.
Testers perform manual testing by executing test cases without automated tools. The tester plays the end-user role, looking at elements like usability, performance, and overall experience. While it’s helpful to get a real-world view of the application and test its limits as a real human user, manual testing can only help in some cases.
Automated testing is usually more cost-efficient and effective than manual tests. Instead of relying on human intervention, automated testing runs pre-written test cases at various points of the development process. They’re faster because they execute in a fraction of the time it would’ve taken a human to complete the same task. Plus, automated testing reduces errors, especially when performing repetitive tasks. But test automation often isn’t a good fit for situations related to user experience, installation, or other more complex tasks that require the logic and reasoning skills of a real person.
Static testing focuses on checking the application’s documentation and files. It examines a product’s static documents to gauge if it’s headed in the right direction. This type of testing might involve inspections, file reviews, and walkthroughs. Static tests are also called verification.
Dynamic testing, however, ensures that the application will function properly in its running state. It executes tests against actively-running parts of the application, seeing if it can withstand user interactions and checking to ensure that it plays well with other aspects of the environment. Dynamic testing is also known as validation.
Now that we’ve covered a few overarching test categories, which specific tests should your organization consider, and how will they help your development process in the long run? There are a ton of software tests out there. Here are a few great ones to get you started.
Unit testing focuses on the smallest-possible pieces of your software. It tests each unit of your application to ensure that it works properly before even attempting to pair it with other components. Unit tests prevent minor errors from traveling downstream, causing more significant problems later. And because they focus on such small application pieces, they make it easy to pinpoint the exact problem. Usually, developers run these tests right after they finish writing each component.
Integration tests occur after your teams form builds by putting components together. They determine if the components can work together correctly. These tests are more complex than unit tests because they require running various application parts together rather than single units.
Functional testing checks that the end product does what it’s supposed to. It compares the application against its product requirements to ensure they align. Similarly to integration testing, functional tests require multiple components to be up and running.
End-to-end testing looks at the running application holistically. It mimics how a real user interacts with the software. Because they’re expensive, teams should run a few extensive end-to-end tests before a software release rather than relying on them to catch more minor errors (leave that to the unit and functional tests!).
User acceptance testing gives an application one last check-through to ensure it’s a good product for end-users. It’s also called beta testing. Rather than professional testers, end-users test the application.
These tests ensure that the application can perform well under various workloads. They gauge if the software will be reliable, efficient, scalable, and responsive, especially when it has to process many requests or handle a lot of traffic.
Smoke testing performs a quick test to ensure that the basic functionality of an application is working well. Usually, teams execute these types of tests right after a deployment to ensure that the app runs well inside its new environment. Smoke testing is also helpful after a new build gets finished to see if the newly-updated application is ready to undergo more expensive, in-depth testing (i.e. end-to-end tests).
Sometimes, a change to the software ends up breaking or degrading the pre-existing components. Regression testing ensures that this doesn’t happen whenever new features are released. It helps teams maintain the same app stability and reliability levels, even after changes.
Software testing might seem intimidating to teams because of the initial setup cost. Automated testing has a pricey entry point, but the return on investment outweighs the initial cost with the following benefits.
By fixing bugs as they arise in the SDLC, you save costs in the long run rather than needing to repair things that break on the user end retrospectively. Plus, teams that perform iterative testing throughout their development lifecycle have a deeper understanding of how their software works. So if something goes wrong in production, there’s a better chance they can pinpoint what happened quickly.
End users are looking for a seamless experience with minimal bugs and performance issues. All of us have been end-users ourselves. We’ve experienced the frustration of a website that takes ages to load or doesn’t do what we need. It’s difficult, if not impossible, to prevent these problems from happening within your app unless you perform testing throughout your process.
If you test early and often, your team is more likely to create clean, functional code. Code defects can become vulnerabilities once your application is live. So, well-functioning code will inherently have less risk than defective or error-prone code.
That’s the bottom line. Your entire business depends on releasing good-quality solutions – in today’s saturated tech market, anything less than excellence won’t beat the competition. If your product runs smoothly and efficiently, it will reap benefits for your entire business.
Here at Launchable, we support developers who incorporate any and all of the mentioned tests into their software development lifecycles. Throughout our own experiences as developers and DevOps specialists, we noticed that while these testing practices were integral to software development teams, they actually had the potential to slow teams down when too many tests were thrown into the mix. Even the best-quality automated testing tools can fall short if development teams don’t take a closer look at their test suites and work to pare them down.
After all, not all tests are built the same, so they shouldn’t all be used the same way. Even if you compare two of the same type of tests (say, two different integration tests), they will function differently. One might be way more accurate and helpful, while another test might be broken and unreliable (aka flaky) and just needs to be retired so it won’t cause problems. Or one might be a better fit for a specific situation, and the other is better in a different case.
We noticed that development teams were running their entire test suites instead of making these distinctions against an update or change – this is time-consuming and counterproductive, often leading to frustrated developers.
That’s why we decided to create our Predictive Test Selection solution. Using machine learning, our automated testing tools identify and run tests with the highest probability of failing based on code and test metadata. As a result, your team doesn’t waste time and resources running tests that aren’t relevant in each situation. Want to learn more about how we can help your team eliminate slow test cycles? Request a Proof of Concept today.