Monthly Archives: April 2022

Despite the ongoing push to “shift left” testing for Agile and DevOps, in-sprint UI test automation remains infeasible. Human-driven UI testing is incredibly intuitive, but UI test automation is considered too slow, too fragile, and way too late for today’s fast-paced teams. Automated UI tests traditionally require a fully-implemented — and stable — UI to be in place before they are created. This means they’re often delayed until a later sprint or scrapped altogether.

But making UI test automation more human-like can resolve these challenges, enabling you to create resilient UI test automation even before a single line of code is written. Read this paper to learn how a new AI-driven approach to this traditionally slow test automation practice can dramatically simplify your testing and get you to “done done” in record time.

Reducing testing costs is often the first thing organizations think about when they consider adopting test automation. After all, software testing is shockingly expensive; it accounts for an average of 23-35% of the overall IT spend, per the latest World Quality Report. Test automation frees humans to focus on more interesting and value-added tasks while routine checks are performed faster, more frequently, and with increased precision.

Clearly, the value is there. However, even open-source testing tools require a resource investment, and that requires the buy-in of someone higher up. How do you convince the business that test automation is really worth the time, effort, and cost required?

This paper outlines what’s needed to build a strong business case for test automation. It covers:

• The top 3 considerations for any test automation business case
• Where most test automation ROI calculations fall short
• A real-world example of an ROI calculation and business case

With every business becoming a digital enterprise, the ability to rapidly release reliable applications is now a core strategic advantage. Are Fortune 500 and equivalent organizations prepared for the digital race ahead? The answer may lie in their software testing process, which can be either a curse or catalyst to speedy innovation.

Continuous Testing enables software to be released 10x faster than traditional testing allows. Yet, its adoption is limited – particularly among large enterprise organizations.

This first-of-its-kind report sheds light on how industry leaders test the software that their business (and the world) relies on. Read this benchmark report to learn:

• How leaders and laggards differ on key software testing and quality metrics
• Where most organizations stand in terms of CI/CD integration, test environment strategy, and other key process elements
• What test design, automation, management, and reporting approaches are trending now
• Organizations’ top priorities for improving their testing over the next 6 months

At the top of every CIO’s mind is how to drive forward innovation at speed, increase levels of productivity, deliver superior and highly scalable digital products and services that increase customer satisfaction, and save costs – all at the same time. Unfortunately, without modernizing your testing with intelligent automation, and treating testing as a strategic component to your digital success, you won’t be as competitive and you can’t innovate as quickly. How do we get there?

AI isn’t the future of testing; it’s the present-day reality, with ever-increasing significance as organizations are propelling into the next normal. New approaches to AI-powered automation relieve many of the lingering pains that have impeded test automation for decades. Using AI to drive the application like a human would, your team can build extremely resilient future-proof tests across any technology.

Read this paper to learn:

• About the 3 core phases of test automation and the barriers broken at each level
• How AI can take the pain out of test design, execution, and maintenance
• How your team can tap AI for earlier, simpler, and more resilient test automation

A recent study from Tricentis on the state of enterprise testing reveals that dev and test cycles are out of sync. The average test cycle time of 23 days is ill-suited for today’s fast-paced development lifecycles. That’s because neither manual testing nor legacy, script-based tools offer enough efficiency or capability to handle today’s accelerated dev cycles. 

Read this white paper and learn: 

• Where legacy, script-based tools fall short 
• How to manage risk and keep pace with modern software development practices 
• How to choose the best functional testing approach for enterprise applications 

As a legacy testing platform, Micro Focus UFT can’t keep pace with today’s agile and DevOps software delivery cycles, which require teams to test earlier in the process, test more frequently, and iterate quickly based on the feedback that testing generates.

Read this white paper to learn:

• What’s inside the Tricentis Continuous Testing Platform
• What a migration from UFT to Tosca looks like
• Five common UFT-to-Tosca migration scenarios
• What two leading enterprises achieved by switching to Tosca
• Your next steps in modernizing your software testing