Digital Transformation

The Future of DevOps Is Here: AI Takes the Wheel

Over the decades, the software development life cycle, or SDLC, has evolved, as developers embrace methodologies such as waterfall, agile development, microservices, DevOps and low-code. As timelines shrink, developers face pressure to roll out products faster, with fewer bugs and vulnerabilities. The role of artificial intelligence, generative AI and machine learning is expanding, accelerating and transforming DevOps as we move toward real-time code generation and automation.

See Also: Managing Infrastructure at Cloud Scale

SDLC and DevOps are two distinct processes, each involving different approaches. SDLC - a more traditional approach - has been around since the early days of programming, dating back to the 1950s. SDLC introduced structured, sequential processes for software development stages, including gathering requirements, system analysis and design, implementation, testing and verification, deployment or rollout, and application maintenance. Each phase in SDLC needs to be completed sequentially before moving to the next. This process is time-consuming - tasks such as fixing bugs take weeks to complete - and causes delays in the rollout of the software product.

The traditional SDLC or waterfall model no longer works, as businesses and consumers now expect frequent updates with new features and regular patching. To address this shortcoming, enterprises have adopted agile methodologies and DevOps to ensure that the SDLC phases are completed simultaneously rather than linearly or sequentially.

AI and ML are transforming the DevOps process by streamlining tasks and improving efficiency for development and operations teams. "Integrating AI/ML into DevOps processes ultimately enhances overall efficiency, reduces manual effort and accelerates time to market for both development and operations teams," said Dattaraj Rao, chief data scientist at Persistent Systems. "AI can take a holistic look at the entire CI/CD pipeline and optimize time-consuming steps or those requiring human intervention."

Rao and his team have developed a gen AI solution for a client. This tool captures knowledge from requirement documents, automatically plans the project, creates user stories in JIRA, writes starter code and builds a project deployment skeleton. "This needs more than standard CI/CD automation. We leverage the power of gen AI agents to 'act like a human' and customize the plan for different scenarios," he said. "We also provide ways to incorporate domain knowledge from expert software managers to fine-tune project plans to meet specific client needs."

Addressing Software Development Challenges With AI and Automation

AI and automation simplify SDLC, optimize DevOps processes and address multiple challenges encountered during software development, including:

• Change management
• Rapid technological change
• Complexity of software systems
• Quality assurance
• Cross-team collaboration
• Resource management
• User experience expectations
• Security concerns

Transforming DevOps Using AI/ML

Rao said AI/ML can transform DevOps in several ways, including:

• AI/ML can be applied at the beginning of the SDLC process using gen AI tools to process business documents, extract user stories and push them to JIRA.
• AI tools can help build better-quality software using coding assistants and can also be used for specific tasks such as generating test cases and documentation.
• AI-driven monitoring tools can analyze system logs and performance metrics in real time to detect anomalies, predict potential failures and alert the operations team before issues affect users. This predictive capability helps reduce downtime and ensure smoother deployments.
• By handling routine tasks, AI allows developers to focus on more complex and creative aspects of their work, leading to faster development cycles and higher-quality software releases.

Predictive Intelligence for Proactive Decision-Making

AI can be applied to various stages of the CI/CD pipeline, said Wing To, general manager of product and development at Digital.ai. "We have been gathering information across the SDLC and have already been using AI, especially for predictive intelligence. By using continuous delivery tools, we can look at planning information and release pipelines, as well as DevOps information, such as test coverage and the number of change failures before production," To said. "We look at patterns and trends within all that information. We use it to do several different predictions. But the ones that we see real interest among our base is understanding the risks associated with delivering software."

Predictive intelligence can also be used in other ways. For instance, project managers can predict whether teams will deliver software on time. This is done by examining historical data and trends related to the quality of their plans and quality of their work as they progress through the delivery process.

The Future of DevOps

Although software development challenges are multifaceted, they are not insurmountable. By using AI/ML technologies, development teams can enhance efficiency, improve quality, foster collaboration and deliver better products that meet the evolving needs of users and businesses alike. As these technologies continue to advance, their integration into software development practices will likely become even more critical in overcoming existing hurdles and embracing future opportunities.

"With the advent of gen AI, we see the DevOps space getting highly transformed. We are seeing the use of agents powered by large language models to provide humanlike experiences to DevOps pipelines and reduce manual touchpoints," Rao said. "From AI creating JIRA stories from project specifications to helping developers write better-quality code and test cases and even creating project artifacts such as documentation and deployment IaC files, we are witnessing a gradual shift in DevOps from automation to intelligence."