摘要
软件产品线工程作为一种高效复用的开发范式，近年来在复杂系统构建中得到了广泛应用，但其核心挑战在于如何有效管理变异点并优化配置以满足多样化需求。本文聚焦于变异管理与配置优化问题，旨在通过系统化方法提升软件产品线的灵活性和可维护性。研究提出了一种基于特征模型的变异分析框架，结合形式化验证技术对变异点进行精确建模，并引入多目标优化算法实现配置方案的自动化生成与评估。实验结果表明，所提方法能够在保证功能完备性的前提下显著降低配置复杂度，同时提高产品生成效率。此外，本文创新性地将机器学习技术融入变异点识别过程，通过数据驱动的方式增强了模型的适应性和鲁棒性。这一改进为解决大规模产品线中的变异爆炸问题提供了新思路。总体而言，本文的研究成果不仅丰富了软件产品线理论体系，还为实际工程应用提供了有力支持，具有重要的学术价值和实践意义。
关键词：软件产品线工程；变异管理；配置优化；特征模型；机器学习
Abstract
Software product line engineering, as an efficient reuse-oriented development paradigm, has been widely applied in the construction of complex systems in recent years. However, its core challenge lies in how to effectively manage variation points and optimize configurations to meet diverse requirements. This study focuses on variation management and configuration optimization, aiming to enhance the flexibility and maintainability of software product lines through a systematic approach. A variation analysis fr amework based on feature models is proposed, which combines formal verification techniques for precise modeling of variation points and introduces multi-ob jective optimization algorithms for automated generation and evaluation of configuration schemes. Experimental results demonstrate that the proposed method significantly reduces configuration complexity while ensuring functional completeness and improving product generation efficiency. Moreover, this study innovatively integrates machine learning techniques into the variation point identification process, enhancing the adaptability and robustness of the model through a data-driven approach. This improvement provides new insights into addressing the issue of variation explosion in large-scale product lines. Overall, the research outcomes not only enrich the theoretical fr amework of software product line engineering but also offer strong support for practical engineering applications, exhibiting significant academic value and practical implications.
Keywords：Software Product Line Engineering; Variation Management; Configuration Optimization; Feature Model; Machine Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 软件产品线工程的研究背景与意义 1
(二) 变异管理与配置优化的现状分析 1
(三) 本文研究方法与技术路线 2
二、变异点识别与建模方法研究 2
(一) 变异点的概念与分类 2
(二) 基于特征模型的变异点建模 3
(三) 自动化变异点识别技术分析 3
(四) 变异点建模的实际应用案例 4
三、配置优化算法设计与实现 4
(一) 配置优化的基本原理与目标 4
(二) 基于约束求解的配置优化方法 5
(三) 智能优化算法在配置中的应用 5
(四) 配置优化算法的性能评估与改进 6
四、工程实践与效果验证 6
(一) 实验环境与数据集构建 6
(二) 变异管理在实际项目中的应用 7
(三) 配置优化的效果验证与分析 7
(四) 研究成果对工程实践的贡献 8
结 论 9
参考文献 10
