Using DevOps Trends in 2021 to Predict the DevOps Trends of 2022
Everyone loves predicting future trends. Aside from offering the whimsical allure of amateur fortune-telling, the right predictions can also prepare you for future business adaptations. However, you don’t need a crystal ball to look into the future of DevOps. This article will use DevOps Trends 2021 to predict DevOps Trends for 2022: no psychic powers required!
3 Key Influences On Shaped DevOps Trends 2021
There are 3 major ongoing developments to consider when discussing DevOps trends:
Beneath each of these components lies an even more significant trend—the trend behind the trends, if you will. The overarching theme here is the accelerated delivery of value through software, which manifested itself in the adoption of DevOps. IT has undergone a massive makeover, from internal processes to cloud infrastructure. Rapid release cycles are the new norm, and businesses have adapted to these trends in different ways.
Hyper-automation in 2022
Despite its self-explanatory nature, allow me to elaborate on hyper-automation to explain why we foresee it playing a large role in 2022 and beyond. Hyper-automation is the mad dash to apply AI capabilities to as many business processes as possible. Although the foundation was laid in DevOps trends 2021, we predict the trend to accelerate in 2022.
This year, hyper-automation presented largely through using AI to facilitate working across multiple technologies, tools, and platforms. In 2022, we predict the expanding popularity of comprehensive and nonlinear approaches, like Copado Robotic Testing.
Evolution of Quality Intelligence
Good decisions are made when proper and adequate data is available. Unfortunately, gathering information takes time and often returns incomplete results. Human judgment must fill in the gaps. DevOps and Agile methodologies addressed this problem by introducing process transparency to the software development life cycle (SDLC). External observers—like managers—now have far greater visibility into development processes.
The future of test automation looks toward finding, recording, and presenting data in a digestible manner. A company that prioritizes transparency and quality intelligence democratizes decision-making. As a bonus, quality intelligence also caters to the need for release cycle speed. Data-backed decisions can be accomplished much more quickly.
Since hyper-automation was among the top DevOps trends in 2021, many production lines have become automated. However, production line automation often fails to consider data governance. In 2022, we expect quality intelligence to catch up. Part of the reason we consider Copado Robotic Testing a comprehensive solution is that it also automates measurement.
Just like hyper-automation and intelligence, dependencies received a lot of attention in 2021. However, in the coming year, organizations will do more than improve their dependency visibility. They’ll leverage that visibility to make the most of their data collection and automation capabilities. So, what does that look like? For most companies, integration will be the center of attention.
Although the goal is unity, there is no single DevOps pipeline. DevOps is part of a complex business system architecture involving various tools, software, and platforms. Let’s look at a possible example:
In this scenario, at least three providers are involved. Each solution provider has its own DevOps team running continuous updates. Because providers generally don’t coordinate their releases, these updates can leave you scrambling to fix integrations, only to see them break again on the next update.
The point: the average organization's business process architecture is highly complex, even at the most basic level. Now that continuous development and deployment have been introduced to the SDLC, individual parts can change rapidly (with little to no human intervention). These conditions require a solution that’s equipped to handle interdependencies.
Using AI to Stay Ahead of the Trends
AI helps us tackle each of the above-mentioned trends at once through the intelligent use of data. In the case of Copado Robotic Testing, AI is used during test runs to find out if any elements have changed unexpectedly. It collects and analyzes data about how your software is used.
This is a critical function because it connects AI and DevOps to provide insight that can be used to predict dependency conflicts. Copado Robotic Testing’s AI can detect possible defects before end users experience them. Then, it provides:
The location of the bug
Potential reasons for the defect
Instructions for resolution