Read Part-1 Here : FinOps
Read Part-2 Here : CD-as-a-Service
AI Powered Code Understanding for Efficient Codebase Automation, Visualization & Developer Team Insights
Introduction
In the world of software development, understanding large codebases can be a daunting task. It requires a lot of time and effort to comprehend the architecture, dependencies, directories, and code changes. However, with AI-powered code understanding, developers can quickly map and automate their application’s services, dependencies, directories, and code changes. This technology helps developers ship faster and prioritize features that drive the most revenue. In this article, we will explore how AI-powered code understanding can help developers understand, build, and refactor applications without any guesswork.
Instant Mapping and Automation
One of the significant benefits of AI-powered code understanding is instant mapping and automation of an app’s services, dependencies, directories, and code changes. With this technology, developers can quickly understand how all the pieces in a system fit together, which influences their ability to move quickly when breaking down modules, breaking up monoliths into services, or changing legacy code. The AI-generated visualizations show all the file dependencies in a codebase, making it easier for developers to comprehend legacy codebases.
For instance, AI-generated code summaries and pull request (PR) summaries of services, PRs, code, and more allow developers to prioritize features that drive revenue and avoid wasting time on less critical tasks.
Proactive Insights Powered by AI
AI-powered code understanding tools provide instant answers to even the most dynamic and complex codebase questions. Developers can ask their codebase questions, and the tool will answer them back. This feature saves time and helps developers resolve issues faster. Moreover, these tools generate actionable insights that maximize team performance.
Another advantage of proactive insights powered by AI is that the Developers can get notified when something important happens with their codebase, even when their focus is elsewhere. The AI-generated code summaries and PR summaries of services, PRs, code, and more help developers resolve questions faster and maximize team performance with actionable insights. This feature is particularly helpful when onboarding new developers or dealing with restructures or RIFs.
Effortlessly Comprehend Legacy Codebases
Codebase automation, visualization, and developer team insights are essential for simplifying the toughest dev challenges. Codebase onboarding, offboarding, refactoring, visibility, and DORA metrics are some of the areas where these tools can help. Codebase onboarding doesn’t just happen when hiring; it occurs whenever developers need to understand how their code works, deal with upstream or downstream dependencies, outages, reorgs, and more. Similarly, codebase offboarding is a challenge that arises when developers leave, and others need to work in the codebase without them.
AI-powered code understanding also simplifies the toughest dev challenges like codebase onboarding, offboarding, and refactoring. Developers can understand their codebase with visualization and automation, allowing teams to onboard to legacy or existing applications faster. With AI-generated walkthroughs of code, developers can self-document services and service flows, generating and sharing how their services work.
Refactoring is another area where code understanding tools can help. Knowing how all the pieces in a system fit together influences the ability to move quickly when breaking down modules, breaking up monoliths into services, or changing legacy code. AI-generated visualizations show all the file dependencies in a codebase, making it easier for developers to understand the architecture and variations in their code.
Visual Code Reviews and Impact Analysis
AI-powered code understanding also enables faster code review and merge time, breaking team knowledge silos. Developers can see the impact of changes before they’re merged and map how a PR affects the rest of their codebase, avoiding last-minute surprises. They can also sanity check their work before merging by visually walking teammates through a map so they know what they were thinking and why.
Visualizing how data flows through services is another crucial feature of code understanding tools. This feature provides a complete understanding of an application’s architecture and enables developers to identify issues quickly. Auto-generated and auto-updated maps of code changes make it easier for developers to keep track of their codebase. Moreover, these tools provide instant summaries of code for better onboarding and understanding.
Understand Variations in Your Architecture
To truly understand a codebase, developers must have a grasp on the variations and changes in architecture that it employs. Without this knowledge, it becomes difficult to pinpoint which parts of the architecture are helping or hindering different features within the application. By monitoring these architectural elements, developers can easily incorporate necessary changes while also justifying any potential costs or risks.
Furthermore, understanding which type of architecture performs best for specific use cases is crucial. This allows developers to make informed decisions when implementing new features or making improvements to existing ones. Utilizing AI-powered code understanding tools can provide filtered views and insights into important aspects such as tech debt and ownership, allowing teams to avoid common pitfalls and ensure optimal performance across their entire codebase.
Cross-Repo Visibility and Dependency Analysis
AI-powered code understanding also provides cross-repo visibility, allowing teams to understand their monolith, service-oriented, monorepo, or any flavor of codebase they have now or in the future. Automated cross-repo visualization tools enable development teams to see inside each other’s codebases, providing a holistic view of the system to communicate the complexity of their work to other people around the business.
Developers spend 60% of their time reading and understanding code because they don’t have a smart way to consume mental models of their code, services, and dependencies. Code understanding tools provide instant mental models of code for rapid understanding, allowing developers to get back to shipping code faster.
Important Success KPIs for a Team of Developers
AI-powered code understanding can help teams save hours in code review and onboarding per year, resulting in dollars saved in code review and onboarding per year. It can also improve quality, security, and reduce costs by enforcing best practices and ensuring that gotchas are followed. AI watches code changes and alerts developers before changes are merged, preventing bugs before they happen.
DORA metrics are critical for tracking the health of a team across critical areas of the software development cycle. These metrics include cycle time, change failure rate, deployment frequency, and other factors that impact team performance. Code understanding tools can help track these metrics and provide insights for continuous improvement.
Conclusion
In conclusion, AI-powered code understanding is a game-changer in software development, simplifying the toughest dev challenges, enhancing collaboration, and improving productivity. With AI-generated visualizations, walkthroughs, and summaries, developers can quickly comprehend legacy codebases, resolve questions faster, and maximize team performance with actionable insights. By automating code knowledge and processes, developers can free themselves from needing to check outdated documentation and operate efficiently and securely.
AI-powered code understanding is the future of software development, and developers who embrace it will be able to deliver secure software faster, understand application complexities, and uncover unknown unknowns in minutes. By automating code knowledge and processes, developers can operate efficiently and securely, enhance collaboration with targeted notifications, prevent bugs before they happen, and enforce processes or new info org-wide.
Read Part-1 Here : FinOps
Read Part-2 Here : CD-as-a-Service