计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 42-49.doi: 10.11896/jsjkx.201200021
所属专题： 复杂系统的软件工程和需求工程
杨立1, 马佳佳1, 江华禧1, 马肖肖1, 梁赓1, 左春1,2
YANG Li1, MA Jia-jia1, JIANG Hua-xi1, MA Xiao-xiao1, LIANG Geng1, ZUO Chun1,2
摘要： 机器学习支撑的系统应用越来越普遍但是此类系统的需求通常难以表达完整且可能存在一些难以检测的冲突使得这些系统通常无法在生产环境中高效满足用户的综合需求.此外对于在实际场景中使用的机器学习系统用户信任通常取决于包含可解释性、公平性等非功能需求在内的综合需求的满足程度且在不同领域内应用机器学习通常有特定的需求为保证需求描述的质量及实施过程的决策带来了挑战.为解决以上问题文中提出了一个机器学习系统的需求建模和决策选择框架包括一个MLS(Machine LearningSystems)需求概念模型和机器学习管道过程元模型以及对训练数据集、算法等组件的决策选择方法旨在规范实际场景中机器学习系统的需求设计、开发和评估.实例研究表明提出的MLS需求描述和实现方法是可行且有效的.
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