Top Influences on CICD: Open Source, Data, AI/ML, and Continuous Quality
Many DevOps teams have moved into some form of continuous integration and continuous delivery (CICD) for software release management. CICD is a method to frequently deliver apps to customers through ongoing automation and continuous monitoring throughout the lifecycle.
CI refers to continuous integration, automating software code changes by regularly building, testing, and merging branches into a shared repository. Meanwhile, CD, or continuous delivery, is the process through which development changes to software are automatically pushed from a code repository to the production environment.
CD can also refer to continuous deployment. Continuous Deployment is the next progression of Continuous Delivery where every change is pushed to production automatically. Continuous Delivery is when development team changes to software are automatically uploaded to a repository. There is a manual gate here. Think of this code is “production ready”, but not automatically deployed. In Continuous Deployment code is automatically pushed to production.
With Continuous Deployment, there is further automation involved for automatically releasing the development changes from the repository to production. Continuous Delivery offers better visibility and communication between developers and business teams. Continuous Delivery is a stepping stone to Continuous Deployment, which also reduces operations teams loads by incorporating automation for deployment.
Related Article: The Evolution of CICD and Future of Software Development
As a whole, CICD empowers DevOps teams to avoid code defects with smaller code changes and fault isolations, increasing test reliability while speeding up release rates. Through the use of automation, CICD increases team transparency and reduces backlogs, offering more standardized feedback loops that lead to faster updates and deployment. In short, CICD supports customer AND developer satisfaction, speeding up release cycles while reducing development friction.
With the adoption of CICD, comes emerging trends. Check out these four huge developments ready to shape, shift, and influence the state of CICD in 2022 and well into the next five years.
- CICD and Open Source Software
- Smarter CICD with Data Driven DevOps
- AI & Machine Learning
- The Movement to Continuous Quality
CICD and Open Source Software
CICD pipelines are no stranger to the influence of open source software. The CICD evolution started from open source software specifically Jenkins - a project founded and led by Launchable’s Co-Founder, Kohsuke. The earliest pipelines were built in Jenkins by our Co-Founders here at Launchable while at CloudBees.
CICD will feel the benefits of on-going collaborations of influential organizations forming committees and interest groups, like the Continuous Delivery Foundation, where Kohsuke (as part of CloudBees) was the first chair of the technical steering committee, and in that capacity he had a board seat during that period. It’s also projected that through 2025, more than 70% of enterprises will increase their IT spending on open source software, compared to their current IT spending. Aligning with open source’s collaboration, openness, and accessibility is the trend towards language agnostic tools.
Smarter CICD with Data Driven DevOps
The biggest opportunity within the DevOps landscape in 2022 and beyond is making data-driven decisions for better quality software. Using machine learning to harness the tsunami of data and finding the right signal for faster feedback loop is the way of the future for data driven DevOps.
Over the past few years, many organizations and DevOps teams have put in the work to fully automate their CICD pipeline. It’s time for another paradigm shift of CICD. Expect savvy DevOps teams to shift from solely focusing on automation to focusing in on smart automation.
Smart automation includes embracing data to make better decisions. This means harnessing the data coming out of CICD and using Machine Learning to find the right opportunities to optimize development. Teams will establish smarter deployment practices by fully utilizing the data exhaust from their delivery pipeline.
AI & Machine Learning
DevOps has a fever, and the only prescription? More Machine Learning.
Software engineering teams constantly face the pressure to deliver newer features faster. While developers could theoretically create new iterations constantly, parts of the DevOps cycle, like testing, are not fully optimized for CICD.
Machine Learning advancements in testing solutions will be the next leading driver to improve speed and efficacy for DevOps teams and deliver developers more time to focus on what truly matters: writing quality code that solves problems and makes lives easier
Additionally, expect the Machine Learning applications of the future to propel better data analysis, and to help developers further optimize software test cycles.
The Movement to Continuous Quality
One more big trend on the horizon involving the movement to optimize the CICD pipeline is the movement to achieve Continuous Quality.
Today, DevOps teams understand that the quality of software is defined by three key components: software functions as planned; the software performs well within the scope of identified use cases; the software has a low rate of defects, or produces few to zero errors during application.
With this trio of ideals in mind, the movement towards Continuous Quality makes perfect sense as a guiding principle for DevOps teams this year. Focusing on the achievement of Continuous Quality, or the balance between speed and quality, allows DevOps team and leaders to speed up the dev feedback loop, improve morale, and create more efficient testing cycles - and results.