Github Copilot

This review synthesizes early evidence (2019–2023) on GitHub Copilot and comparable LLM assistants, focusing on their impact on developer productivity, code correctness, and adoption in real workflows. Across controlled studies and field reports, assistants consistently accelerate boilerplate creation, scaffolding, and routine transformations, with smaller or mixed effects on open-ended design and debugging. Gains are highly contingent on task type, developer experience, prompt quality, and integration with tests and review. Correctness and security remain variable; unvetted suggestions can introduce defects or insecure patterns, underscoring the need for linters, unit/property tests, and human code review. Usability, privacy, and governance shape adoption, while CI/CD integration and standardized PR practices convert raw speed into reliable delivery. We highlight evaluation pitfalls in measuring "productivity," and outline near-term priorities: governed use, outcome-oriented metrics, AI-augmented review, and expansion to richer IDE and organizational contexts.