Techniques and Prediction Models for Sustainable Product-Line Engineering

可持续产品线工程的技术和预测模型

• 批准号: 221150666
• 负责人: Professor Dr.-Ing. Sven Apel
• 金额: --
• 依托单位: Lehrstuhl für Software Engineering
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/221150666?language=en
• 关键词: Techniques; Prediction; Models; Sustainable; Product

Software product line engineering has gained considerable momentum in recent years, both in industry and in academia. Companies and institutions such as NASA, Hewlett Packard, General Motors, Boeing, Nokia, and Philips apply product-line technology with great success to sustain their development by broadening their product portfolio, improving software quality, shorting time to market, and being able to react faster to market changes. However, pursuing a product-line approach implies often an up-front investment for future benefits. Product-line developers have to anticipate which features will be desired by customers in the future. So, prediction models play an important role to avoid uneconomic developments. However, contemporary prediction models largely ignore structural and behavioral properties of the architecture and implementation assets of a product line. For example, modifying the transaction management of a database system is by far more expensive and risky than modifying its command-line interface. We propose to rethink contemporary prediction models and to employ state-of-the-art analysis techniques to create a richer knowledge base for predictions based on implementation knowledge, including software metrics, static analysis, mining techniques, measurements of non-functional properties, and feature-interaction analysis.

软件产品线工程近年来在工业界和学术界都获得了相当大的发展势头。美国国家航空航天局、惠普、通用汽车、波音、诺基亚和飞利浦等公司和机构成功地应用了产品线技术，通过扩大产品组合、提高软件质量、缩短上市时间以及能够更快地对市场变化作出反应来维持其发展。然而，采用生产线方法往往意味着为未来利益进行前期投资。产品线开发人员必须预测客户将来需要哪些功能。因此，预测模型在避免不经济的发展中起着重要的作用。然而，当代的预测模型在很大程度上忽略了产品线的架构和实现资产的结构和行为属性。例如，修改数据库系统的事务管理比修改其命令行界面要昂贵得多，风险也大得多。我们建议重新思考当代的预测模型，并采用最先进的分析技术，以创建一个更丰富的知识库，预测的基础上实现的知识，包括软件度量，静态分析，挖掘技术，测量的非功能属性，和功能交互分析。