pair programming

Evidence (6)

• 3InfoQ·10/1/2026research
  How to Build Software Engineering Skills in the AI Era

  The article argues that software engineering skill development still requires slowing down, even as generative AI speeds up delivery. It emphasizes that engineers need to understand how systems work, and that senior developers can help juniors through deliberate coaching and learning techniques.

• 5Martin Fowler·9/2/2026framework_update
  Martin Fowler Says Code Review Is Solving the Wrong Problems

  Martin Fowler argues that AI-driven code generation is making traditional code review too overloaded to serve as the main quality gate. He suggests moving feedback earlier through pairing, design sessions, trunk-based development, automated testing, static analysis, fitness functions, and security scanning, while reserving review for issues that cannot be checked automatically.

• 2InfoQ·8/24/2026product_launch
  Podcast on Brownfield Codebases, Mob Programming, and AI-Assisted Coding

  In this podcast, Asgaut Mjølne Söderbom and Ola Hast discuss how their software engineering practices have evolved beyond continuous deployment and pair engineering. They also describe experiments with Claude Code and why they view it as useful for many tasks, but not as a replacement for coding in brownfield codebases.

• 4Martin Fowler·7/31/2026research
  AI Is Turning Developers into Conductors

  Martin Fowler argues that AI has shifted the main bottleneck in software development from writing code to managing human attention and coordination. He describes developers increasingly orchestrating multiple AI agents in parallel, with their role resembling a conductor who provides context, evaluates output, and directs work across the system.

• 5The New Stack·7/19/2026research
  AI Is Pushing Code Review Upstream

  The article argues that traditional pull-request-based code review is becoming a poor fit for AI-assisted software development, where large amounts of code are generated quickly. It suggests teams should review intent, specs, and acceptance criteria earlier in the workflow rather than waiting until merge time.

• 5Martin Fowler·4/21/2026research
  Thoughtworks Radar highlights AI, security, and code review

  Thoughtworks published the 34th volume of its Technology Radar, which is dominated by AI-related topics but also revisits established practices such as pair programming, mutation testing, DORA metrics, and accessibility. The piece emphasizes security risks in agentic tools, especially prompt injection and broad permission access, and argues that durable code still needs regular human review.