The Rise and Fall(?) of Software Engineering

TL;DR

The paper surveys how rapid AI advances—especially transformers and large language models—are transforming software engineering (SE). It traces the historical arc of SE and AI, analyzes AI4SE developments from early DL efforts to modern foundation models, and discusses both the opportunities and risks of AI-enabled SE. Key contributions include clarifying the distinction between traditional DL methods and foundation models, highlighting the role of pretraining and RLHF, and outlining a human‑in‑the‑loop agenda covering data governance, interpretability, IP, and education. The work emphasizes that while AI can boost productivity, software engineers remain essential for architectural design, production quality, ethical considerations, and responsible deployment, necessitating curriculum and policy adaptations. Overall, it provides a practical roadmap for researchers and practitioners to harness AI in SE while preserving human creativity and accountability.

Abstract

Over the last ten years, the realm of Artificial Intelligence (AI) has experienced an explosion of revolutionary breakthroughs, transforming what seemed like a far-off dream into a reality that is now deeply embedded in our everyday lives. AI's widespread impact is revolutionizing virtually all aspects of human life, and software engineering (SE) is no exception. As we explore this changing landscape, we are faced with questions about what the future holds for SE and how AI will reshape the roles, duties, and methodologies within the field. The introduction of these groundbreaking technologies highlights the inevitable shift towards a new paradigm, suggesting a future where AI's capabilities may redefine the boundaries of SE, potentially even more than human input. In this paper, we aim at outlining the key elements that, based on our expertise, are vital for the smooth integration of AI into SE, all while preserving the intrinsic human creativity that has been the driving force behind the field. First, we provide a brief description of SE and AI evolution. Afterward, we delve into the intricate interplay between AI-driven automation and human innovation, exploring how these two components can work together to advance SE practices to new methods and standards.