Bryan Cole, Director of Product Engineering at Tricentis, outlines how businesses can ensure the quality and successful delivery of their cloud-based apps.
To stay ahead in today’s digital marketplace, enterprises must deliver new and quality products to market faster than ever. Cloud-based technologies that allow for rapid development and scalable performance have proven critical, with spending on public cloud services predicted to reach nearly $600 billion by the end of last year.
However, these cloud-based technologies are not without pitfalls. The quality of an application can make or break a business’s reputation as users publicise reviews and ratings. Modern application development prioritises shorter release cycles, with features being delivered at rapid speeds. To maintain velocity and ensure quality, testers must streamline processes and rely on methodologies and automation tools exclusively geared for speed and efficiency.
Cloud-based, automated testing enables teams to work more efficiently and identify opportunities to streamline app delivery while maintaining quality and performance. Performance testing is valuable to be gained early in the development cycle, while continuous risk-based and regression testing is critical throughout application delivery.
Successful app delivery requires collaboration between multiple teams and tools and a unified end-product vision. This article will look at the best practices for quickly deploying quality cloud-based applications.
Prioritise quality assurance
The demand for faster application delivery puts a lot of strain on quality assurance teams, who are battling to produce better, more usable applications with fewer glitches at faster rates. But where is this pressure coming from?
The truth is that to stay competitive in today’s digital-forward marketplace. Enterprises need to deliver new quality digital products faster than ever before. We’ve seen an explosion in the development of cloud-based apps because they allow rapid development and responsive updates. This is essential in meeting users’ expectations for new features in secure universal apps available from anywhere on any device.
However, release velocity should never come at the cost of performance. Businesses must ensure application quality in order to reap the advantages of cloud-based app development without creating risk to their brand reputation. Quality assurance means comprehensive testing – and this is where the bottleneck lies.
While the development process for cloud-based apps is streamlined through Agile and DevOps methods, thorough testing represents a major hurdle. Multiple browser vendors and versions introduce user interface variations, while various mobile device platforms and versions require functional verification for every application change. Doing this well leads to a continuous development cycle, which requires parallel development and testing efforts.
Utilise automated testing
Sophisticated test automation allows organisations to monitor and assess issues in real time or even stop them before they occur, averting any significant disruptions. As such, advanced automation is the key to accelerating cloud-based app adoption while maintaining quality and resilience.
Using a four-part cloud adoption framework, building, migration, and performance testing allows enterprises to migrate existing applications to the cloud and simultaneously develop new cloud-native and mobile applications to enable better customer engagement. All while maintaining quality and protecting the organisation’s brand.
Organisations must align business goals with testing methods and quality ambitions to successfully leverage automated SaaS testing solutions. Whether your priority is accelerating release cycles, scaling to reach more customers, or integrating with SAP or another ERP system, it’s important to choose the right automation tools to get you there.
SaaS testing solutions allow you to test from anywhere at any time, which enables applications to connect business processes seamlessly, enabling an end-user workflow by reducing production bugs and stabilising across environments to support business continuity.
Companies must also recognise that, for the most part, legacy applications are not designed for cloud infrastructure, so they will likely need to refactor critical applications to be cloud-native rather than migrate them. Fortunately, cloud-native app templates can accelerate new app development while retaining unique end-user qualities.
Businesses must also deploy consistent, repeatable application testing parallel to application development. Regression testing is often only added after an app is shipped to its first customers, when it is especially hard to implement, so it’s important to instill the need for testing by including it in initial app planning.
Building in testing from the outset means that IT teams can test both regression cases and load testing, ensuring the application performs smoothly and correctly.
As cloud-based apps grow and changes need to be made, it is crucial for testing infrastructure to adapt with it, accommodating new browsers and mobile devices and load testing that matches expected activity rates.
Make the most of generative AI
The introduction of generative AI into application development and testing will be the next step in speeding up cloud-based app delivery with quality built-in.
One of the big problems in the QA space is that there’s never enough time to test everything. You have to be picky and prioritise. That will potentially fall away with AI engines when they start getting really good – particularly in cloud environments where automated performance testing ensures migrated apps will scale and perform under load.
Teams can instruct the AI to crawl an application and determine how many tests are needed to ensure that every interactable object on any page has tests built for it.
Generative AI works much faster on tasks than humans can, and so long as quality assurance teams are diligent, thorough, and clear about what they are asking the AI to do, the limit to their productivity becomes almost limitless.
Quality assurance teams will move from the low-level engineering activities of creating individual test assets to the much more executive function of managing and executing those test assets. This means they will instruct AI engines to recreate and redevelop existing assets to accomplish specific business objectives.
So, the benefits of introducing generative AI into cloud-based app development are clear but come with the major caveat of requiring a business model with quality assurance at the core of its operations.
AI can only be expected to benefit the business if the best practice is in place to ensure its output is reliable, compliant, secure, and ultimately controlled by humans with the expert knowledge to spot mistakes.
Enterprise opportunity for cloud-based apps
With customer expectations higher than ever, testers and developers’ task is to deliver easy-to-use, functional applications that can be tailored and updated quickly without compromising quality. This is a big ask and has made utilising the cloud necessary since it provides opportunities to scale quickly.
Taking steps to create quality best practices in cloud-based app development is critical for enterprise organisations. They must recognise the need to integrate test automation – which is crucial in increasing release speeds and improving application quality – into their operations to ensure quality and performance are never compromised.
Doing so will ultimately help enterprises to run more efficiently to meet their bottom line. By adopting end-to-end quality assurance, enterprises can de-risk and accelerate business transformation to ensure successful outcomes.
Faster test cycles that embrace automation and no-code capabilities accelerate the delivery of new capabilities to the business, allowing teams to do more with less, increasing business risk coverage and reducing production defects for higher quality releases for increased business confidence. Rigorous planning, continuous monitoring, and regression testing have become critical throughout the development cycle and must remain a priority.
As new technologies, such as generative AI, become increasingly integrated into software delivery practices, there is even more potential to unlock greater efficiencies in the delivery of cloud-based apps, so long as these best practices in QA are adhered to.
