让软件产品线更好支持机器学习组件的集成与复用。
Enhancing software product lines with machine learning components
- 提出结构化框架，系统建模包含机器学习的软件产品线变异性。
- 通过工具实现部分功能，支持在产品线中管理机器学习组件。
- 适合关注AI与软件工程融合的开发者和架构师。
现代软件系统因机器学习（ML）的进步而越来越多地集成其能力，以提升数据驱动决策水平。然而，这种集成给软件工程带来了新挑战，尤其是在软件产品线（SPL）中，引入机器学习组件后，变量管理和复用变得更加复杂。尽管已有方法分别解决了SPL中的变异性管理以及孤立系统中机器学习组件的集成问题，但两者交叉领域的研究仍较少，尤其缺乏对集成机器学习组件的SPL中变异性进行建模与管理的支持。为此，本文提出一个结构化框架，扩展软件产品线工程，以促进机器学习组件的集成。该框架通过系统化建模变异性与复用性，支持具备机器学习能力的SPL设计。部分功能已在VariaMos工具中实现。
原文摘要 · Abstract (English)
Modern software systems increasingly integrate machine learning (ML) due to its advancements and ability to enhance data-driven decision-making. However, this integration introduces significant challenges for software engineering, especially in software product lines (SPLs), where managing variability and reuse becomes more complex with the inclusion of ML components. Although existing approaches have addressed variability management in SPLs and the integration of ML components in isolated systems, few have explored the intersection of both domains. Specifically, there is limited support for modeling and managing variability in SPLs that incorporate ML components. To bridge this gap, this article proposes a structured framework designed to extend Software Product Line engineering, facilitating the integration of ML components. It facilitates the design of SPLs with ML capabilities by enabling systematic modeling of variability and reuse. The proposal has been partially implemented with the VariaMos tool.
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