一句话：提出 MP-MODA 框架，显式表示可变性、影响与决策点，支持反馈驱动的多准则全局决策。
现代软件开发要求组织快速适应持续变化的环境，但利益相关者缺乏自动化支持来进行全局决策：解空间可变性日益增大，相关可变性与决策点常缺乏显式表示，决策对利益相关者和解空间的影响也不确定，导致决策过程缓慢、易错且偏向局部知识。本文提出 Multi-Plane Models and Data（MP-MODA）框架，显式表示和管理可变性、影响与决策点，在反馈驱动开发过程中支持多利益相关者的多准则决策自动化与工具支持，并通过反馈循环降低不确定性。论文阐述该框架的概念结构，讨论其潜在收益，并列举工具支持的自动化与分析所面临的关键挑战。
To succeed with the development of modern software, organizations must have the agility to adapt faster to constantly evolving environments to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, business, development, and IT. However, stakeholders do not have sufficient automated support for global decision making, considering the increasing variability of the solution space, the frequent lack of explicit representation of its associated variability and decision points, and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. The Multi-Plane Models and Data (MP-MODA) framework explicitly represents and manages variability, impacts, and decision points. It enables automation and tool support in aid of a multi-criteria decision making process involving different stakeholders within a feedback-driven software development process where feedback cycles aim to reduce uncertainty. We present the conceptual structure of the framework, discuss its potential benefits, and enumerate key challenges related to tool supported automation and analysis within MP-MODA.
@inproceedings{paperbot7287,
title = {Global Decision Making Over Deep Variability in Feedback-Driven Software Development},
author = {Joerg Kienzle and Benoit Combemale and Gunter Mussbacher and Omar Alam and Francis Bordeleau and Lola Burgueno and Gregor Engels and Jessie Galasso and Jean-Marc Jézéquel and Bettina Kemme and Sébastien Mosser and Houari Sahraoui and Maximilian Schiedermeier and Eugene Syriani},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3559551}
}