CloudBees CEO Anuj Kapur told attendees at a DevOps World event today that with developers spending only 30% of their time writing code the current state of software development in enterprise IT organizations is a disaster.
After more than 14 years of effort, the promise of DevOps—in terms of being able to accelerate the rate at which applications are being deployed—remains largely academic, said Kapur. In fact, the effort to shift more responsibility for application security further left toward developers has only increased the amount of cognitive load and reduced the amount of time available to write code, he noted.
However, with the rise of generative artificial intelligence (AI), an inflection point that will dramatically increase the velocity at which applications are being built and deployed has arrived, said Kapur. The challenge will be achieving that goal without increasing the cognitive load on developers. That cognitive overload results in 70% of developers’ time not being productive within organizations that often hire thousands of developers, he noted.
Despite all the DevOps issues that need to be addressed, AI advances promise improvement. The overall DevOps market is still relatively young, given the current level of adoption, said Kapur. “We continue to believe the market is early,” he said.
Today, CloudBees took the wraps off the first major update to the open source Jenkins continuous integration/continuous delivery (CI/CD) platform to have been made in the past several years. At the same time, the company also unveiled a DevSecOps platform based on Kubernetes that is optimized for building and deploying cloud-native applications based on portable Tekton pipelines. That latter platform provides the foundation through which CloudBees will, in the months ahead, apply generative AI to software engineering to, for example, create unit tests on the fly and automate rollbacks.
In addition, DevSecOps capabilities will be extended all the way out to the integrated development environments (IDE) to reduce the cognitive load of developers.
The overall goal is to reduce the number of manual processes that create bottlenecks that make it challenging to manage DevOps at scale.
Criticism of the level of developer productivity enabled by DevOps compared to other development approaches needs to be tempered, said Tapabrata Pal, vice president of architecture for Fidelity Investments, because it still represents a significant advance. There is still clearly too much toil, but the issues that impede the pace at which developers can effectively write code tend to be more cultural than technical, he added.
Organizations are not inclined to automatically trust the code created by developers, so consequently, there is still a lot of friction in the DevOps process, noted Pal.
In theory, advances in AI should reduce that friction, but it’s still early days in terms of the large language models (LLMs) that drive generative AI platforms and their ability to create reliable code, he added. That should improve as LLMs are specifically trained using high-quality code, but in the meantime, the pace at which substandard code might be generated could overwhelm DevOps processes until AI is applied there as well, said Pal.
Thomas Haver, master software engineer for M&T Bank, added that while assisted AI technologies will have a major impact, it’s not reasonable to expect large organizations to absorb them overnight. Patience will be required to ensure advances are made in ways that are both secure and sustainable, he added. In some cases, organizations may opt to build their own LLMs to achieve that goal, noted Haver. “You should not expect to change on a dime,” he said. “It will take time to see the fruits of our labor.”
While many DevOps challenges need to be resolved, the benefits continue to outweigh the cost and effort required to succeed. The hope is that, with the help of AI, the return on those investments will continue to accrue.