Augmenting software engineering with AI. The ai4se taxonomy and its use

Engineering notes

These capabilities are largely powered by ’big code’: vast repositories of open-source software that now form the basis of data-driven, empirical SE and automated quality assurance.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract Although model-driven software engineering (MDSE) has proven effective in managing complex systems, its industrial adoption remains limited by the substantial maintenance overhead required for models and the specialised skills demanded of developers. Meanwhile, advances in artificial intelligence (AI), particularly generative and agentic AI, have shown great promise in automating code-related tasks such as comprehension, generation, and defect detection. These capabilities are largely powered by ’big code’: vast repositories of open-source software that now form the basis of data-driven, empirical SE and automated quality assurance. This paper aims to synthesise these two domains by exploring the integration of AI into model-driven practices. It provides a comprehensive overview of the current state of AI-augmented software engineering and introduces a novel taxonomy ’ ai4se ’ to classify and connect diverse AI applications within the field. On this basis, the paper proposes a vision for ’big models’ in software engineering (SE), an approach designed to leverage the structural advantages of MDSE alongside the scalability of AI. Finally, the paper discusses the pair modelling paradigm as a collaborative framework for the MDSE industry, designed to enhance software quality through human–AI partnership.

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