Software testing has been outpaced by evolution of Cloud & DevOps technology
A look into the disproportionate advancements in DevOps technologies vs. software testing tools, and how a modern testing solution like Functionize can bridge that gap using AI and ML.
Developer tools have evolved significantly over the past 17 years. In the past, everyone used physical servers. Now, with cloud infrastructure, continuous integration/continuous delivery (CI/CD), and containers, developers can move faster than ever.
However, testing tools have not kept up with the advancements in developer tools. Selenium was created in 2004. But modern web development has changed so much since then. And certain market trends have also exacerbated this problem.
Although there have been some test automation advancements, there’s still a massive gap between developer tools and software testing tools. Test automation today is still painful and leads to huge amounts of test debt. But Functionize can help you bridge that gap with artificial intelligence/machine learning-based technology.
Cloud rate of release, CI/CD, containers outpace testing
Increasingly, organizations are moving all or at least some of their business processes to the cloud. However, even though cloud infrastructure enables enterprises to modernize the delivery environment and speed development, their customers still want the software to perform the way it’s supposed to perform in production, no matter how it’s deployed.
Customers expect the applications to be secure, resilient, and highly available or they’ll seek out your competitors. And the key to meeting those expectations is testing.
Rearchitecting business apps for the cloud is no easy feat. It takes a tremendous amount of work to integrate, test, and continuously validate them — work that’s often revised during tests of each new release.
Although that’s always been the case, the rate of release in the modern cloud environment is growing exponentially. That’s because DevOps teams are using fast service-based integration and data feeds, automated deployment pipelines, infrastructure-as-code definitions, CI/CD, and containers that can run anywhere.
For example, CI/CD enables DevOps teams to focus on code quality and security as well as meet business requirements because deployment steps are automated. This in turn allows companies to ship software quickly and efficiently.
However, CI/CD requires continuous testing to find issues earlier because the goal is to deliver quality applications and code to users. But delivering quality software isn’t just about increasing the speed of software deployment and releases; it’s also about developing and implementing testing methods that ensure you deliver error-free products to your customers as quickly as possible.
While testing is crucial for every DevOps process, the current approaches to testing are less than ideal. Test automation typically relies on Selenium. Selenium modernized testing when it was released in 2004, as for the first time it was possible for anyone to automate the repetitive user interface (UI) testing that is essential before a company can release a product. However, Selenium doesn’t really cut it from a DevOps perspective.
Comprehensive regression testing for modern web apps can potentially require testers to run hundreds of tests on as many as 30 browser/device combinations. And it takes several minutes to run each test. As such, running these tests one at a time isn’t really feasible because each release will likely take months. Although Selenium now lets you run tests in the cloud, this requires a lot of maintenance, and it still struggles to run at the scale necessary to keep pace with your CI/CD pipeline.
Market trends make this problem even worse
As if the fact that software testing has been outpaced by the evolution of cloud and DevOps technology isn’t bad enough, the effects of globalization and the COVID-19 pandemic have made things even worse on software development and testing.
While it’s true that globalization has made it easier for organizations to tap into global markets to recruit software engineers and testers, it has also increased competition. And that has made it even harder for companies to hire talented automation engineers who know how to code, making scripted test automation really hard to take on.
The globalized workforce as well as the pandemic have changed the way we work and collaborate with each other forever as working remotely will likely become part of our new normal even after social distancing restrictions slowly go away.
Many distributed software testers and developers struggle with the myriad challenges that come with working remotely, including how to collaborate virtually. Not being in the same location as developers makes it more difficult for testers to keep up with all the changes to applications that developers are making. For example when developers change UI elements — UI testing is a critical element of the software testing cycle — it’s even harder for testers to react and fix broken test scripts.
Working remotely also makes testing multi-user apps challenging because it requires numerous testers to synchronize user interactions to simulate real-world scenarios. Take the case of a web conferencing app that requires the host and the guest to coordinate to test real-world scenarios — coordination is difficult if testers are working remotely.
As mentioned, test automation is painful and leads to significant test debt. The more automated tests you create, the more time it takes to maintain those tests. Test automation is supposed to speed up testing, but the upkeep alone can be crippling for testing teams. Test automation debt makes it difficult to improve an application’s performance, correct defects, or adjust to new trends. Automating existing tests is necessary for faster software development.
These problems around testing and test automation are not new. People have always known about them and many have tried fixing them. Recently, this has involved applying artificial intelligence (AI) and machine learning (ML) to testing.
How Functionize uses AI/ML-based technology to help bridge the test automation gap
Machine learning is transforming how we live and work. It enables us to create self-driving cars and powers the intelligent assistants in our homes. And machine learning also offers us a better way to do software testing.
Machine learning boosts artificial intelligence by applying algorithms that enable the tool to automatically improve by collecting massive amounts of data produced by testing. Machine learning makes decisions based on data that has been observed previously. Collecting data is key to making decisions and machine learning is extremely valuable in this regard as it initially requires some data for analysis and adapts as more data is collected.
This is a key facet of AI as intelligence necessitates adjusting decision-making as learning improves. Unlike traditional test automation, Functionize’s system is constantly fine-tuning a detailed machine learning model of your application based on your tests. We capture every piece of detail for every test step, including screenshots, Document Object Model and CSS data, and even timing data for all the elements on the page. We collect and analyze these millions of data points every time your test runs.
Your test definition is an ever-expanding dataset from every execution. This allows the system to understand the intent behind your tests and adapt intelligently to change. When you update your application, your tests self-heal, saving your team valuable time to create more automated tests.
Functionize provides a suite of intelligent test automation tools powered by machine learning. Our tools allow anyone to build automation, test smarter, and release faster. Traditional automated tests are brittle, constantly break, and require manual upkeep. With Functionize, you can empower your teams to build AI-powered tests that don’t break and will give you peace of mind, so you can focus on delivering great software and let machine learning supercharge your tests.
As noted, test automation debt hinders the productivity of testing teams. They are constantly debugging and fixing test scripts that failed because some part of the user interface was changed or updated. Frequently, teams spend more time on this than any other part of testing, even though it’s totally unproductive.
Functionize uses machine learning and artificial intelligence to avoid Selenium’s limitations. Self-healing features help tests evolve in parallel with your application, dramatically reducing test maintenance. That means test automation debt is soon eliminated. Functionize can even help automate tests that simply can’t be automated with Selenium, including two-factor authentication and file verifications.
Although organizations that have adopted DevOps have progressed in many areas, testing has not improved at the pace or scale required. In fact, it was not that long ago that testing relied on test automation tools that were ancient.
However, advancements in ML and AI are now enabling intelligent test automation. As such, testing is becoming fully integrated at every stage in the software lifecycle. The upshot: better software, released faster, and with fewer wasted cycles. Try Functionize and sign up for a free 14-day trial today.