A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI
Domenico Amalfitano, Andreas Metzger, Marco Autili, Tommaso Fulcini, Tobias Hey, Jan Keim, Patrizio Pelliccione, Vincenzo Scotti, Anne Koziolek, Raffaela Mirandola, Andreas Vogelsang
TL;DR
This study reframes GenAI's integration into software engineering along two axes—what is augmented (process vs. product) and how autonomous the augmentation is (passive vs. active)—to define four forms: Copilot, Teammate, GenAIware, and GenAI Robot. Using Design Science Research across three cycles, it builds a transparent, multi-source Roadmap validated by workshop discussions, rapid literature reviews, and peer input, and then distills form-specific and cross-form research opportunities. McLuhan's tetrads guide a nuanced analysis of gains, reversals, retreival of past ideas, and obsolescence across each form, yielding a set of ten predictions for SE in 2030. The roadmap emphasizes hybrid human-AI collaboration, governance, accountability, and runtime verification as central to deploying GenAI in a trustworthy, impactful manner. Overall, the work offers a structured, evidence-based framework and actionable directions for advancing GenAI-augmented SE while anticipating significant socio-technical risks.
Abstract
Generative AI (GenAI) is rapidly transforming software engineering (SE) practices, influencing how SE processes are executed, as well as how software systems are developed, operated, and evolved. This paper applies design science research to build a roadmap for GenAI-augmented SE. The process consists of three cycles that incrementally integrate multiple sources of evidence, including collaborative discussions from the FSE 2025 "Software Engineering 2030" workshop, rapid literature reviews, and external feedback sessions involving peers. McLuhan's tetrads were used as a conceptual instrument to systematically capture the transforming effects of GenAI on SE processes and software products.The resulting roadmap identifies four fundamental forms of GenAI augmentation in SE and systematically characterizes their related research challenges and opportunities. These insights are then consolidated into a set of future research directions. By grounding the roadmap in a rigorous multi-cycle process and cross-validating it among independent author teams and peers, the study provides a transparent and reproducible foundation for analyzing how GenAI affects SE processes, methods and tools, and for framing future research within this rapidly evolving area. Based on these findings, the article finally makes ten predictions for SE in the year 2030.