How to Infuse AI Across the DevOps Lifecycle, applying AI in planning, building, testing, releasing, and operating for greater speed, accuracy, and value.
A recent report from IDC revealed a striking reality in software development: developers allocate a mere 16% of their working hours to actual coding. This statistic challenges the narrow focus on code generation capabilities when evaluating AI’s potential impact on software development. While code-assisted development has captured headlines, the true transformative power of artificial intelligence lies in its application across the entire development lifecycle.
By expanding our perspective beyond coding assistance, organizations can infuse AI in each phase of application development. Generative AI excels at summarizing large volumes of information, creating initial document drafts from collected materials, deconstructing large documents into components, and identifying gaps and ambiguities in written content. These capabilities can be effectively integrated throughout the application lifecycle management phases: plan, build, test, deliver, and operate.
Plan
Business Analysts can leverage AI to collect inputs from various stakeholders, segment and summarize common themes from emails, request forms, and meeting transcripts. The technology significantly reduces the time required for feedback review and analysis while providing references to source material within summaries.
For agile methodologies, AI effectively breaks down larger documents into components, identifying and creating multiple features from requirements documentation and subsequently dividing these features into discrete User Stories.
Build
Code-generating Copilots represent a well-established DevOps use case. These tools effectively generate small classes and methods, though currently face limitations with larger classes. Maximum benefit from Generative AI requires breaking solutions into smaller components for class and method generation.
Generative AI also demonstrates proficiency in building low-code components. Metadata such as workflows, object models, and permissions, typically represented in XML, are readily generated by AI systems.
Test
AI generates comprehensive test plans for features, user stories, or entire end-to-end processes. The technology creates company-standard test plans based on existing security and compliance documentation, incorporating all requirements with specific references.
During regression testing, AI analyzes results to differentiate between actual bugs and broken tests due to changed expected behaviors. The system can then heal existing tests or suggest code modifications to resolve bugs.
Release
For systems lacking API support for certain settings, AI generates Robotic Process Automation (RPA) scripts to automate manual steps in the release process. Additionally, AI can refine scripts recorded during manual processes for inclusion in automated procedures.
AI produces targeted Release Notes specifically for stories that successfully complete the approval process, along with training materials for new features. Early creation of these materials provides clear documentation for User Acceptance Test participants.
Operate
AI Workspaces enable the creation of expert AI support agents for specific business processes or applications. This approach enhances stakeholder support without requiring users to search through FAQs or release notes. Users simply ask questions directly, including general inquiries about new releases.
AI capabilities extend well beyond coding throughout the DevOps process, enhancing productivity for developers, Business Analysts, testers, and Release Managers. The implementation of AI across the development lifecycle results in accelerated time-to-value, reduced defects, and improved internal stakeholder support. These improvements collectively generate significant economic benefits across the organization.
A quote or advice from the author:
AI-generated code isn’t just about algorithms; it’s about how well we communicate needs through better prompts and documentation. As automation grows, quality and compliance are more important than ever.
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