一句话：提出行为鲁棒化技术，将设计改造形式化为多目标优化，使系统在环境偏差下仍满足关键性质。
论文关注运行于高度动态、不确定环境中的软件系统，希望即便环境出现偏差，系统仍能建立其最关键的性质要求。作者提出“行为鲁棒化”（behavioral robustification）技术：给定系统与环境的行为模型以及用户指定的偏差集合，系统性地、严格地生成一个重新设计，使其在环境发生这些偏差时依然满足目标性质。该方法把鲁棒化问题表述为多目标优化问题——在阻止偏差环境引发性质违反的同时，最大化保留原有功能，并最小化相对原设计的改动代价。作者在电子投票机与安全关键界面两类案例研究中验证了方法的有效性。
Modern software systems are deployed in a highly dynamic, uncertain environment. Ideally, a system that is robust should be capable of establishing its most critical requirements even in the presence of possible deviations in the environment. We propose a technique called behavioral robustification, which involves systematically and rigorously improving the robustness of a design against potential deviations. Given behavioral models of a system and its environment, along with a set of user-specified deviations, our robustification method produces a redesign that is capable of satisfying a desired property even when the environment exhibits those deviations. In particular, we describe how the robustification problem can be formulated as a multi-objective optimization problem, where the goal is to restrict the deviating environment from causing a violation of a desired property, while maximizing the amount of existing functionality and minimizing the cost of changes to the original design. We demonstrate the effectiveness of our approach on case studies involving the robustness of an electronic voting machine and safety-critical interfaces.
@inproceedings{ZhangSMBGK23,
title = {Robustification of Behavioral Designs against Environmental Deviations},
author = {Changjian Zhang and Tarang Saluja and Rômulo Meira-Góes and Matthew Bolton and David Garlan and Eunsuk Kang},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00046}
}