Testing and Automation Predictions: A Closer Look at Gartner’s Forecasts for the Coming Years

Another year has come and gone, and many of us are reflecting on the past and looking ahead to see how we can best prepare for the future. Let’s see what Gartner has to say about it in terms of the application development world.

As last year was beginning to wind down, the research firm published the Predicts 2017: Application Development report authored by analysts Mike West, Maritess Sobejana, Joachim Herschmann, and Keith James Mann. It includes key findings in application development that will impact organizations and recommendations for application leaders to provide insight for planning in the years to come.

These Gartner analysts make multiple predictions for 2017 and beyond, but two stand out to me as worth highlighting.

One, they’ve observed the growing importance of testing and quality assurance (QA) in all stages of the development lifecycle and predict that, “By 2020, DevOps initiatives will cause 50% of enterprises to implement continuous testing using frameworks and open-source tools.” (Predicts 2017: Application Development, Gartner, Nov. 28, 2016)

And two, this report finds that advanced predictive and prescriptive analytics are gaining momentum in application development and that organizations will begin leveraging these more frequently to improve the quality of software development.

“By 2020, 50% of IT organizations will apply advanced analytics in application development to improve application quality and speed of delivery.” (Predicts 2017: Application Development, Gartner, Nov. 28, 2016)

Let’s start with discussing the first prediction, the increased adoption of “continuous testing.”

The move by organizations to DevOps practices has broken down the old model of testing processes since quality is now an assumed part of DevOps. Testing is no longer a monolithic stage that determines the release schedule, as it used to be in a Waterfall model. Testing is now baked into every part of the DevOps cycle, making it easier to get higher code coverage and allowing for faster releases.

What’s more, testing has become automated, moving it even farther left in the pipeline.

Now we see more unit testing and system integration testing earlier in the process. This change in testing processes benefits the organization by reducing operational costs from outages. It also contributes to the mission of DevOps by accelerating feedback between Development and Operations for better speed and quality.

One example of how this could play out for the benefit of a software development enterprise team is setting up automated testing to be triggered when a user story in development is ready for QA. This leads us to Gartner’s second prediction around analytics.

In order to trigger automated testing appropriately, your organization needs analytics from a number of places, and across tools. When a number of point solutions exist for different stages in the lifecycle—plan, monitor, and release, for example—in order to achieve efficiency across the entire DevOps lifecycle and make data-driven decisions, these tools need to be rationalized, and analytics must span the gaps.

The analysts at Gartner point out this challenge, stating, “DevOps toolchains evolving without a plan and encompassing the entire DevOps process result in disparate, overlapping tools that can be difficult to integrate and automate across the different DevOps areas. Point solutions for automated testing are becoming a constraint in the overall process.” (Predicts 2017: Application Development, Gartner, Nov. 28, 2016)

Tools integration and analytics are closely linked now as business leaders strive to achieve greater efficiency in how applications are made, deployed, and maintained through DevOps, continuous integration, and continuous feedback.

Analytics information helps teams prioritize tasks based on data, for example, about feature popularity or security threats. And all of this reduces wasted time and efforts while upholding quality.

“To keep up with the business demand to deliver applications quickly, efficiently, and of high quality, IT organizations will need to accelerate the shift in focus to advanced analysis. They will need to adopt a fact-driven analytics culture that is pushed down from the CEO and executive team to effectively reduce waste and increase the velocity and quality of the application. This in turn will lead to improved customer satisfaction and significant savings in time and resources.” (Predicts 2017: Application Development, Gartner, Nov. 28, 2016)

At the end of the day, any good business leader will want to know whether the efforts of his or her company are well spent, and the key to achieving that efficiency assurance in business is data analytics.

Analytics can also help support continuous testing and automated testing, which Gartner predicts will gain traction in the next few years.

This current gap in the DevOps and application development market—the need for analytics across the toolchain—is one of the reasons we decided to create the DevOps Lifecycle Manager (DLM), which provides that integration and analytics layer to span all tools and stages in the DevOps lifecycle from plan to release and back.

To share your comments on Gartner’s insights or other thoughts on testing and analytics, use the comments section below. We’d love to hear from you.

 

Eric Robertson

Robertson is responsible for the DevOps product line at CollabNet. Prior to CollabNet, Eric served as Director of services and portfolio management for enterprise solutions at Unisys. Eric joined Cisco through a company acquisition, where he led product management for Cisco's cloud automation and SAP ALM extension offerings. Eric has successfully held product development, services and management roles with enterprises and start-ups and has provided consulting services to Fortune 500 companies. Eric holds a patent in the area of virtualization and a Master's Degree in Electrical and Software Engineering from University of Texas in Austin and continues to further his research in domain- specific languages, and intelligent automation.

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