2018 DevOps Predictions - Part 8
January 08, 2018

DevOps experts — analysts and consultants, users and the top vendors — offer thoughtful, insightful, and sometimes controversial predictions on how DevOps and related technologies will evolve and impact business in 2018. Part 8, the final installment, covers testing.

Start with 2018 DevOps Predictions - Part 1

Start with 2018 DevOps Predictions - Part 2

Start with 2018 DevOps Predictions - Part 3

Start with 2018 DevOps Predictions - Part 4

Start with 2018 DevOps Predictions - Part 5

Start with 2018 DevOps Predictions - Part 6

Start with 2018 DevOps Predictions - Part 7


In 2018, we'll see more developers turn to continuous testing (once considered a bottleneck) in the form of machine learning and data-driven testing to help push through the DevOps pipeline. With more products hitting the shelves every day, like smartphones and IoT devices (as well as their operating systems), there's a need for smarter applications and faster release cycles when it comes to those applications — which can be months, weeks or even days. In the new year, developers will see continuous testing, and technical enablers like cloud, open-source frameworks and ML/AI, will help them to deliver at a much faster pace, allowing them to address bugs and course-correct in real-time.
Eran Kinsbruner
Technical Evangelist, Perfecto Mobile

While the testing phase has been long overlooked and mostly confined to the QA domain, it can't be overlooked anymore. Lack of proper performance testing is the primary reason for speed and performance issues in production. Performance testing can't be limited to QA but has to be continuous to ensure that the continuous integration/continuous delivery pipelines produces quality software with high performance. Performance testing needs to shift-left to include development and integration phases, and at the same time needs to shift right to ensure seamless continuity and visibility between dev and test and production.
Paola Moretto
Founder and CEO, Nouvola


Continuous testing gets its due in the mobile arena: The year-over-year growth of mobile shopping has finally jettisoned traditional desktop purchasing as king of the holiday hill, according to Adobe Analytics. With this swing, a greater emphasis on mobile testing is needed to ensure app experiences are up to snuff. In order for developers to keep pace with rapid deployments associated with mobile, continuous testing will need to play a pivotal role. A mechanism that's struggled to find a home in the development lifecycle, continuous testing will finally pay major dividends in 2018 with the rise of mobile.
Leo Laskin
Senior Solutions Architect, Sauce Labs


Using continuous testing best practices — and their associated technologies — DevOps teams will migrate away from legacy testing solutions and leverage open source and cloud-based testing solutions that support shift left methodologies (shifting testing to earlier in the software development lifecycle) and automate the progression of code through each phase of the SDLC — testers will become automation engineers. With this move, DevOps teams will positively impact many of their most important digital KPIs, including:
■ More frequent deployment of new and enhanced features
■ Faster time-to-market for highest-value digital updates
■ Optimum mitigation of digital business risks such as downtime, laggy response, data errors, and breaches
■ Lower digital business costs
■ A more attractive and engaging work environment for high-skill developers
Aruna Ravichandran
VP of DevOps Solution Marketing and Management, CA Technologies

Traditionally mainframe-heavy industries like banking, retail and government are rolling out cutting-edge application functionalities that leverage their mainframe. One that comes to mind is a leading European bank which recently introduced a mobile app allowing customers to make cardless withdrawals at ATMs. Ultimately, applications like this touch the mainframe at the back-end, and in order for them to be widely adopted, they must be fast and reliable, requiring code testing across the application. As mainframe developers and testers retire, we predict more organizations will equip generations of developers with automated testing tools, helping them maintain a consistently high level of mainframe code quality as this subset continues to be a major factor in overall application quality and performance.
David Rizzo
VP of Product Development, Compuware

Organizations don't automate everything yet -- but develop a culture of intelligent automation: Though we've already begun to witness a cultural shift in how organizations approach testing, 2018 will see a continued shift in the role manual testers play — and an increased demand for intelligent automators. Organizations are departing from the traditional testing paradigm of taking manual test cases and writing automation on those tests and shifting toward revamped tooling and intelligent frameworks to implement automation from the get-go. Automation is the future: and the upcoming year will see businesses increasingly deploy automation within their organization — beyond just using automated deployments.
Leo Laskin
Senior Solutions Architect, Sauce Labs


With mixed success in 2017, AI testing will take a major step forward next year. Currently, AI testing can help develop tests, but the key breakthrough will feature AI testing that can easily identify bugs within test failures without needing QA staffers to pinpoint issues themselves. This will cut down on cyclical review time and speed up deployments, pushing efficiency boundaries for release times.
Leo Laskin
Senior Solutions Architect, Sauce Labs


In 2018 the API economy will continue to gather steam as organizations look to unlock the power of APIs to accelerate digital transformation. This will result in API testing moving from niche to necessity as organizations strive to monitor the user experience at the service level as well as through the application.
Antony Edwards
CTO, Testplant


In 2018, DevOps will drive monitoring and testing to converge, and testing will become monitoring. Time to market will remain a critical success metric for business, however they also need to ensure high quality, great performance, and an exceptional user experience, despite the ever-shrinking window to get an app out there. This means that testing is so continuous that it will become monitoring. Therefore, in 2018 performing tests continuously on a running application in production to monitor its performance and functionality, will become standard.
Antony Edwards
CTO, Testplant


The New Year will bring a heavier focus on Test Data Management as businesses continue to find the availability of test data to be one of the most significant constraints that drives lead time in test cycles. Additionally, solid test data management practices will be key to overcoming compliance hurdles and avoiding huge fines associated with the EU General Data Protection Regulation, which comes into effect on May 25, 2018. Whether organizations mask it, clone it, mine it or generate synthetic data, they will need to understand the structure of data. At an enterprise scale, organizations will have to automate the fulfillment of everyday test data requests. Automation will reduce the request fulfillment from days and weeks down to minutes. Next, there needs to be some thought about how the consumption model will change as testing continues to shift left and right. As more testing shifts left going forward, consumption by developers will increase — which leads to consumption of the data via APIs.
Aruna Ravichandran
VP of DevOps Solution Marketing and Management, CA Technologies


We will continue to see IT organizations improve their delivery execution, their team member job satisfaction, and their value to the business as they combine the state of the art in process (DevOps), technology (containers) and application architectures (microservices). Containers and microservices provide the nimbleness and agility that an organization needs to rapidly change to meet customer needs. DevOps provides the speed of delivery for these changes by automating the lifecycle. However, in 2018, we will see IT architectures explode in complexity thanks to these containers and microservices. This will require adoption of another technology, impact analysis, which will allow IT to understand the "butterfly effect" that one change can have throughout the architecture. Without impact or dependency analysis, things will quickly spiral out of control.
Dan Juengst
Principal Technology Evangelist, OutSystems

The Application Performance and Quality Testing Gap Grows

In 2018, we'll see the chasm in organizations' ability to implement application performance and quality testing grow. Those that are able to follow an Acceptance Test Driven Development (ATDD) and Test Driven Development (TDD) approach will have the capacity to scale back their human-led testing efforts. Most of the requirement specifications will be done in Domain Specific Languages (DSL) such as Gherkin, which also incorporates Acceptance Testing. Since these requirements are created and automated before the source code is written, less effort is required than in traditional System and System Integration Testing. Organizations that can learn to use these DSLs and let go of their traditional ways of documenting requirements and objectives (specifications, requirements, analysis documents) will reap the rewards of reduced time needed to write, review and execute test cases, allowing them to focus on other activities with a higher ROI. Conversely, some organizations will be unable to leverage ATDD and/or TDD approaches due to the usage of COTS software, development platforms that don't allow test automation, or other technical restraints. We will primarily see this issue with organizations that use, configure, and customize enterprise resource planning (ERP) software packages, or that are highly dependent on legacy software that does not easily allow test automation approaches. These businesses will be required to further upscale either their manual testing or their GUI-based test automation. As a result, they will at a minimum be unable to lower costs — if not see increased costs — as well as need to change their staffing from manual to technical test automation specialists that work in collaboration with developers.
Michaël Pilaeten
Learning & Development Manager, CTG