Using AI Across the Dev Lifecycle
summary
Survey of ~5,000 devs shows 78% use AI somewhere and claims AI speeds only ~21% of a developer day, creating a downstream bottleneck; proposes platform-level AI that ties planning, code review, security scans and monitoring together so automated agents create a merge request, run enforced dependency and SAST/DAST scans, summarize long issues, propose MR fixes for CVEs and even act as first reviewer. Demonstrates GitLab as an example and a productized offering called GitLab Duo, plus the idea of a one‑click "troubleshoot" root‑cause analyzer.
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chapters (13)
- 0:00AI in the Dev Lifecycle
- 1:12Dev team goals and AI
- 3:00AI as a disruptor
- 4:24Developer productivity with AI
- 6:13AI beyond developers
- 7:41AI tools across pipeline
- 8:23Automated DevSecOps flow
- 9:26Contextual AI visibility
- 11:35AI for code security
- 13:46AI transparency and root cause
- 15:11Security scans with AI
- 16:18AI models in SDLC
- 17:01GitLab Duo and Q&A
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