Video summary

How AI Is Changing Code Reviews & Software Development

Main summary

Key takeaways

Technology

Main technological concepts & evolution of code review

Core software “building blocks”

  • Code
  • Documentation
  • Architecture

Historical eras of code review

Each era shifts what code reviews focus on:

  1. Fagan inspection (structured, team-based)

    • Originated by Michael Fagan (IBM).
    • Conducted as line-by-line, team inspections of code and documentation.
    • Very time intensive, but designed to emphasize consistency and quality.

  2. Agile / paired programming (on-the-fly)

    • Reviews become embedded into development as people build and check each other’s work.
    • Still largely focused on implementation correctness, described as syntax-focused.

  3. Pull request era (repo + diffs + approvals)

    • Work is submitted via pull requests and merged after merge approval.
    • Adds versioning and diff-based review complexity.
    • Shifts toward consensus reviews: groups evaluate versions/differences and approve at scale.

  4. CI/CD automation era (system-assisted checks)

    • CI/CD pipelines add automated building, validation, and governance layers.
    • Automated checks cover:
      • Code quality
      • Vulnerabilities/security
      • Compliance (internal/external/governmental)
    • Reviews become system reviews: not only humans approve diffs—the system verifies requirements.

What changes in the AI era

LLMs + “intelligence” in the development/review loop

AI (via LLMs) broadens analysis and partially automates development artifacts, including:

  • Writing code
  • Writing/updating documentation
  • Drafting architectures
  • Helping manage:
    • Merging
    • Repo diffs
    • Integration workflows
  • Extending automation layers (code quality, security, and compliance) with AI-assisted understanding

Shift in review focus

  • From implementation → to business outcomes and requirement fulfillment
  • Humans provide context and judgment:
    • Define what “success” means for the business
    • Validate whether AI-generated work matches those outcomes
    • Iterate by refining prompts and making trade-offs

Outcome/evidence-based reviews

Reviews become evidence-based, using:

  • Automated checks from the CI/CD era
  • AI analysis tied to requirements + expected outcomes
  • Runtime evidence (i.e., “looking at run time evidence”)

The progression described is:

  • Syntax reviews → Consensus reviews → System reviews → Outcome reviews

Final framing: future code review is about validating intent, outcomes, and business impact, not merely reviewing more code—especially as codebases become increasingly AI-generated.

Main speakers/sources (from subtitles)

  • Michael Fagan (IBM) — credited as originator of the Fagan inspection approach.
  • No other specific speaker name is clearly identified in the provided subtitles.

Original video