Get an overview of - and solutions to - the major challenges that we face in this age of DevOps
Testing has always been a bottleneck in the software development life cycle (SDLC). By its very nature, QA is about arresting a production process to check that requirements are being met. Mitigating risk, checking, and double-checking every line of code, running system tests, unit tests, component tests - these are tasks that are antithetical to the concept of continuous delivery. Maintaining a constant stream of releases when everything passing through the pipeline has to be tested leaves us with something of a paradox.
And this problem is now more acute than ever. In today’s development environments, test teams are being asked to get the code through the QA bottleneck faster than ever before. The danger with this is that the faster we try to go through the bottleneck, the riskier we make the journey.
That said, technology that can be harnessed by QA teams has not stood still. Gartner points out we are in an age of test hyperautomation. Now before you roll your eyes at our use of this lofty term, let us point out its significance: hyperautomation (sorry, said it twice now) is the combination of Robotic Process Automation (RPA), Natural Language Programing (NLP), AI, Machine Learning (ML), codeless tools, and a few others that are not relevant to this discussion. Rapid advancement in these fields and using them in combination offers us the ability to overcome the testing challenges we face in the age of CI/CD and DevOps - even if the name is a little hyperbolic. Let’s look in closer detail at the types of problems testing faces in the age of CI/CD and DevOps and how to overcome them.
The increasing complexity of applications has made them more difficult to test. We now have many more potential user journeys to explore, map, and test, and this takes a lot of time. There’s also an increasing number of potential device/browser configurations, and they all have to be tested, too.
Solution: RPA is the use of software with AI and ML capabilities to handle high-volume, repeatable tasks that previously required humans to perform. RPA bots can fully explore an application in minutes while gathering data on the state of the app, creating user journeys and snapshots, and detecting changes. The bots can be directed towards a URL and let loose to explore at will, or they can be directed to interact with the app in a predefined way. RPA allows us to move testing towards being system-driven and autonomous, increasing overall QA capacity and accelerating software development velocity.
Any combination of manual testing/code-based automation creates a silo in your development life cycle and an overdependence on technical expertise. Code needs to be written before testing can start. And if your QA experts leave the company for whatever reason, it can create a huge legacy problem.
Solution: NLP is a branch of AI that deals with the interaction between computers and humans using natural language. The ultimate objective of NLP is to read, decipher, and understand human language in valuable ways. In QA, we can use it as a codeless way of authoring tests. By removing the necessity for coding, we can make QA accessible to non-technical personnel, manual testers, and even those in business roles. This means we can reduce our reliance on siloed, code-based automation and increase collaboration by involving many more people in the QA process. NLP is a big part of developing Intelligent Quality Assistance because it offers a highly accessible way of sharing the responsibility for quality. As an added bonus, you can also write tests before a single line of code has been written.
One of the biggest barriers to scaling code-based automation is test maintenance. When you have thousands of automated tests, the burden of repairing them every time there is even a small change in the application becomes unsustainable.
Solution: ML is an application of AI that gives systems the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves. This offers the ability to create tests that maintain themselves by learning from their experiences.
Even with the best QA team, the construction of a custom-made, code-based framework takes time and money. There is a large startup cost associated with this approach, as well as a significant delay. Code-based automation requires technical expertise. Not only are QA engineers expensive, they’re in short supply. Even if you’re totally committed to hiring only the finest artisanal QA engineers there simply aren’t enough to go around.
Solution: Codeless technology has come a long way to the extent that it is finally delivering on its promise in test automation. For the first time, anyone can create robust tests with fast feedback that actually work, even on the most dynamic of applications.
The pressure to make new releases can result in low coverage because there isn’t time to automate a sufficient number of tests. Less sophisticated features end up being developed because it’s faster and easier to get them through the QA bottleneck.
Solution: A combination of the above (cough cough, test hyperautomation). By leveraging recent technological advancements, QA teams can adopt a strategy that tests more and faster. And check this out: they can concentrate on finding problems and bugs that our current state of technology can’t. Everybody wins!
This is certainly a good list of bottlenecked problems, but at least they all have their solutions. But, are you worried about getting all these solutions in one place? That’s what Virtuoso is here for! We can offer the perfect pairing to every problem, all in one neat, easy-to-use software solution. With zero ramp-up, Virtuoso will decant all your testing pains away, and there’s no need to let it breathe.
