【24h】

Towards Consistency Analysis between Formal and Informal Software Architecture Artefacts

机译:朝着正式和非正式软件架构人工制品之间的一致性分析

获取原文

摘要

Documenting the architecture of a software system is important, especially to capture reasoning and design decisions. A lot of tacit knowledge can easily get lost when the documentation is incomplete, resulting in threats for the software system's success and increased costs. However, software architecture documentation is often missing or outdated. One explanation for this phenomenon is the tedious and costly process of creating documentation in comparison to (perceived) low benefits. In this paper, we first present our long-term vision, where we plan to persist information from any sources, e.g. from whiteboard discussions, to avoid losing crucial information about a system. A core problem in this vision is the possible inconsistency of information from different sources. A major challenge of ensuring consistency is the consistency between formal artefacts, i.e. models, and informal documentation. We plan to address consistency analyses between models and textual natural language artefacts using natural language understanding and plan to include knowledge bases to improve these analyses. After extracting information out of the natural language documents, we plan to create traceability links and check whether statements within the textual documentation are consistent with the software architecture models. In this paper, we also outline our requirements for evaluating our approach with the help of a community-wide infrastructure and how our approach can be used to maintain community-wide case studies.
机译:记录软件系统的架构很重要,尤其是捕获推理和设计决策。当文档不完整时,很多默契知识可以很容易地丢失,导致软件系统的成功威胁和增加成本。但是,软件架构文档通常丢失或过时。这种现象的一个解释是与(感知)低利益相比,创建文档的繁琐和昂贵的过程。在本文中,我们首先展示了我们的长期愿景,在那里我们计划从任何来源持续到任何来源的信息。从白板讨论,避免对系统的关键信息丢失。这种愿景中的核心问题是来自不同来源的信息可能不一致。确保一致性的主要挑战是正式人工制品之间的一致性,即模型和非正式文件。我们计划使用自然语言理解的模型和文本自然语言人工制品之间的一致性分析,并计划包括知识库,以改善这些分析。在从自然语言文档中提取信息后,我们计划创建可跟踪性链接并检查文本文档中的语句是否与软件架构模型一致。在本文中,我们还概述了我们在社区范围内的基础设施的帮助下评估我们的方法的要求以及我们的方法如何用于维持社区范围的案例研究。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号