AI-Assisted Software Development Life Cycle Management

Overview

Use of AI assistants across the entire embedded SDLC — requirements elaboration, architecture and design review, code generation, automated review, test authoring, documentation, and ongoing field-issue triage — rather than just code completion. The practice is about how the team's process changes, not which model they use.

Benefits

• Compresses the requirements-to-prototype cycle dramatically
• Maintains documentation, tests, and code in lockstep instead of drift
• Surfaces design alternatives and risks earlier
• Lowers the cost of writing the unglamorous artifacts (tests, docs, ADRs) that teams normally skip

Limitations & Risks

• Quality varies sharply by phase — codegen is mature, requirements and verification work less so
• IP, traceability, and audit needs differ by industry (medical, automotive, defense have hard constraints)
• Process discipline matters more than tool choice — undisciplined teams ship more code, not better code

Recommended Actions

Map the practice to your existing SDLC phase by phase, identify two or three phases where AI changes the cost curve most for your team (often test authoring and code review), pilot agentic workflows there, and treat AI-produced artifacts the same way you'd treat a junior engineer's output — review, accept, or revise.

Additional Notes

Broader than the prior 'AI-Assisted Code Generation' framing. Pairs with Agentic AI Coding Workflows (#26), Automated Code Review (#25), and SBOM/CRA practices for downstream traceability needs.

References & Links

• https://docs.claude.com/en/docs/claude-code/overview
• https://docs.cursor.com/
• https://github.com/features/copilot