论文标题
使用模糊图和整数编程的依赖性感知软件需求选择
Dependency-Aware Software Requirements Selection using Fuzzy Graphs and Integer Programming
论文作者
论文摘要
软件需求选择旨在在尊重项目限制的同时找到最佳的需求子集。但是，需求的价值可能取决于最佳子集中的其他要求。但是，这种价值依赖性是不精确的，难以捕获。在本文中，我们提出了一种基于整数编程和模糊图的方法，以说明价值依赖性及其在软件需求选择中的不精确。所提出的方法（称为依赖性软件需求选择（DARS））由三个组成部分组成：（i）一种用于识别用户偏好中价值依赖性的自动化技术，（ii）基于模糊图的建模技术，该技术允许捕获价值依赖性和（III III）的不可或缺的模型（iii），并捕获（IIIII），以及（IIIIII）（IIIII）（iii II III）（III II III）。从这些偏好中确定，以降低软件项目中价值损失的风险。通过研究一个现实世界软件项目，我们的工作得到了验证。结果表明，我们提出的方法减少了软件项目的价值损失，并且可扩展到大需求集。
Software requirements selection aims to find an optimal subset of the requirements with the highest value while respecting the project constraints. But the value of a requirement may depend on the presence or absence of other requirements in the optimal subset. Such Value Dependencies, however, are imprecise and hard to capture. In this paper, we propose a method based on integer programming and fuzzy graphs to account for value dependencies and their imprecision in software requirements selection. The proposed method, referred to as Dependency-Aware Software Requirements Selection (DARS), is comprised of three components: (i) an automated technique for the identification of value dependencies from user preferences, (ii) a modeling technique based on fuzzy graphs that allows for capturing the imprecision of value dependencies, and (iii) an Integer Linear Programming (ILP) model that takes into account user preferences and value dependencies identified from those preferences to reduce the risk of value loss in software projects. Our work is verified by studying a real-world software project. The results show that our proposed method reduces the value loss in software projects and is scalable to large requirement sets.
