The AI Native Dev - from Copilot today to AI Native Software Development tomorrow The Missing Gap In Workflows For AI Devs | Baruch Sadogursky
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Jul 1, 2025 Baruch Sadogursky, Head of Developer Relations at TuxCare, dives deep into the critical role of automated integrity in AI outputs. He discusses the “intent-integrity gap” between human goals and LLM outputs, highlighting why developers must maintain their roles amidst evolving AI technologies. Baruch emphasizes the need for rigorous testing and structured methodologies in code generation, while also exploring the importance of adaptable specifications in this new landscape. Trust in AI-generated code is crucial, and he underscores the balance between creativity and accuracy in LLMs.
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Trust Issues with AI Code
- Developers naturally distrust code they didn't write, causing skepticism toward AI-generated code.
- This lack of trust leads to less thorough code reviews and fewer tests, risking software integrity.
Tests Are Code Guardrails
- Tests act as essential guardrails to establish trust in generated code.
- If generated tests can be trusted, the code passing them can also be trusted.
Review Specs with Stakeholders
- To trust generated tests, we must review them even if testing seems tedious.
- Include broader stakeholders like product managers and business people in reviewing specs and tests.