Upgrade to Pro — share decks privately, control downloads, hide ads and more …

The Future of CICD Continuous Code Quality usin...

The Future of CICD Continuous Code Quality using AI

The future of CI/CD is all about continuous code quality with AI. Some platforms already offer AI-powered features like code suggestions and vulnerability detection. However, we can leverage Google Cloud's Gemini platform for a truly cloud-powered approach. Imagine a CI/CD job that checks out code changes, reads your team's custom coding style rules (modularity, logging, etc.), and uses Gemini's API to analyse the code against these rules. This would alert reviewers to potential style violations, ensuring a consistent codebase and smoother collaboration. For even deeper analysis, we could consider training a custom Large Language Model (LLM) on Vertex AI, which could improve its code review capabilities as your codebase grows. This combined approach paves the way for a future where AI becomes an essential co-pilot in the software development process

Rajani Ekunde

November 25, 2024
Tweet

More Decks by Rajani Ekunde

Other Decks in Technology

Transcript

  1. Agenda • Understanding CI/CD • Code Quality Challenges in CI/CD

    Pipelines • Leveraging AI for Automated Code Analysis and Review in CI/CD • Workflow • Ollama & Gemini Approach • Demo • Challenges and Considerations
  2. Understanding CI/CD • CI - Continuous Integration • CD -

    Continuous Deployment • Automates integration, testing, and deployment in development workflows. • Ensures faster delivery with reduced manual intervention and errors.
  3. Code Quality Challenges in CI/CD Pipelines • Limited time for

    comprehensive manual code reviews. • Inconsistent adherence to coding standards across teams. • Delayed detection of critical security vulnerabilities and flaws. • Frequent code changes leading to integration conflicts. • Maintaining quality while meeting rapid deployment demands.
  4. Leveraging AI for Automated Code Analysis and Review in CI/CD

    Workflows • AI quickly finds and fixes common coding mistakes. • Helps maintain coding standards across all projects. • Detects security vulnerabilities early in the process. • Saves time by reducing manual review efforts. • Provides suggestions to improve code performance.
  5. Understanding the Workflow 1.Setup a set of Rules (RULES.MD) 2.API

    Key for AI Studio or Vertex AI 3.GPU CI/CD Runners 4.Basic Python 5.A can-do attitude!
  6. Challenges and Considerations • AI may produce false positives, requiring

    manual verification. • Training AI models needs large, high-quality datasets. • Integration with existing CI/CD tools can be complex. • Ensuring AI adapts to evolving coding practices and trends. • Balancing automation with manual reviews for critical code areas.