Software Engineering in the Age of Large Language Models: An Evidence-Informed Playbook for Practitioners

Abstract

The use of large language models (LLMs) is being introduced into requirements, code generation, testing, maintenance, and documentation processes, but most IT organizations have yet to establish a practical and evidence-based methodology regarding when these tools are value added, when they become risky, and how to regulate their usage. The article is a synthesis of recent empirical research, surveys of developers, and guidance on the use of LLMs in software engineering and translates that information into a playbook of guidance that can be applied by practitioners. The primary contribution of the article is a staged adoption framework, which includes explore, pilot, and scale, supported with lightweight survey templates, small-task assessment designs, and accept/edit/reject logging practices that organizations can adopt to produce their own context-specific evidence. The objective is to facilitate disciplined, open-minded adoption of LLMs in actual software engineering environments.