面向高效仿真后端与友好前端的绑定与半自动接口生成：以流行病用例的演示
Manual binding and semi-automated interface generation for efficient simulation backends with user-friendly frontends: A~demonstration for epidemic use cases
Maximilian Betz, Daniel-Michel Richter, Daniel Abele, René Schmieding, Henrik Zunker, Martin J. Kühn
AI 速览：作者展示如何将大型传染病建模C++框架高效暴露给Python，并提出基于抽象语法树分析C++代码的半自动接口生成方法。
Challenges and chances of mathematical software are directly entangled with the programming language used. Depending on the background of developers and users, conflicts between different goals may arise. While several, e.g., scripting languages allow a low barrier and user-friendly entry to mathematical modeling and simulation, corresponding implementations will most likely not allow timely outcomes for detailed simulations. In infectious disease modeling, while several large-scale software frameworks rely on C or C++ implementations, most models are implemented in Python or R. While the latter allow for fast prototyping and more interactive development, it has been shown for several applications that C or C++ implementations outperform plain Python or R implementations by factors between one and three orders of magnitude. In particular, in times of pandemics, the urgent need for swift estimation of the system's dynamics makes the use of large-scale models in these noncompiled languages prohibitive. In order to create synergies and close the gap between scientists from the application and the computational domain, binding libraries allow to execute entire, compiled software frameworks from languages such as Python. However, with iterative model development, extension, or addition of new models, implementing bindings can represent another relevant barrier to developers of the C or C++ backends. In this paper, we show how a large-scale software framework with a multitude of infectious disease models can be efficiently exposed to Python. In addition, we suggest a semi-automated way to generate interface code by analyzing generic C++ code through abstract syntax trees, enabling a modularized approach working with intermediate representations.