SHF: Small: Tactic-Centric Traceability Models for Preserving Architectural Quality

软件密集型系统通常是围绕一组架构决策设计的。这些决策通常基于众所周知的体系结构策略，这些策略共同塑造已交付解决方案的结构、行为、属性、过程和治理。不幸的是，架构质量经常随着时间的推移而降低，因为正在进行的维护活动是为了纠正错误、提高性能，或者为了响应不断变化的需求而调整系统。本研究提出了一种新颖实用的方法来解决架构退化问题，该方法基于战术可追溯性信息模型（tTIMs）的新概念。tTIMS提供语义类型化的、可重用的和可扩展的可追溯性链接，旨在显著地简化可追溯性过程，降低其成本，扩大规模以支持大型和复杂项目中的可追溯性和架构保存，并促进软件维护人员对底层设计原理的可视化。所提出的技术将具有影响各种高性能和安全关键软件系统的潜力。该项目做出了几项重要贡献。首先，它为将体系结构评估和跟踪检索领域的现有技术集成到维护过程中提供了潜在的转换解决方案。其次，它开发了新的算法和技术，用于以语义类型跟踪链接的可重用基础结构的形式跨项目捕获和重用可跟踪性知识。第三，它提供了新的可视化技术，用于在通用软件工程任务的上下文中与开发人员和维护人员交流底层架构决策及其相互关系。第四，它通过结合使用跟踪检索、机器学习和结构分析技术，提出了尖端的跟踪检索解决方案，以便检测与架构策略相关的类，然后根据策略中的特定角色对这些类进行分类。最后，它利用这些信息自动生成体系结构上重要的可追溯性链接。

Software-intensive systems are typically designed around a set of architectural decisions. These decisions are often based on well-known architectural tactics, which work together to shape the structure, behavior, properties, processes, and governance of the delivered solution. Unfortunately, architectural quality often degrades over time as ongoing maintenance activities are undertaken to correct faults, improve performance, or to adapt the system in response to changing requirements. This research presents a novel and practical approach for addressing the problem of architectural degradation based on a new concept of tactic Traceability Information Models (tTIMs). tTIMS provide semantically typed, reusable, and extensible traceability links, and are designed to significantly simplify the traceability process, reduce its costs, scale up to support traceability and architectural preservation in large and complex projects, and facilitate the visualization of underlying design rationales to the software maintainer. The proposed techniques will have the potential to impact a wide variety of high-performance and safety critical software systems.The project makes several important contributions. First, it provides a potentially transformative solution for integrating existing techniques from the field of architectural assessment and trace retrieval into the maintenance process. Second it develops new algorithms and techniques for capturing and reusing traceability knowledge across projects in the form of a reusable infrastructure of semantically typed traceability links. Third, it provides new visualization techniques for communicating underlying architectural decisions and their interrelationships to developers and maintainers within the context of common software engineering tasks. Fourth, it advances cutting edge trace-retrieval solutions by utilizing a combination of trace retrieval, machine learning, and structural analysis techniques in order to detect classes related to architectural tactics and then to classify those classes according to specific roles in the tactic. Finally, it utilizes this information to automatically generate architecturally significant traceability links.