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进行设计的。这些决策通常基于众所周知的体系结构策略，这些策略共同塑造所交付解决方案的结构、行为、属性、流程和治理。不幸的是，随着进行持续的维护活动以纠正故障、提高性能或调整系统以响应不断变化的需求，体系结构质量通常会随着时间的推移而降低。本研究基于战术可追溯性信息模型(TTIM)的新概念，提出了一种解决体系结构退化问题的新的实用方法。TTIMS提供语义类型的、可重用的和可扩展的可追溯性链接，旨在显著简化可追溯性过程，降低其成本，向上扩展以支持大型和复杂项目中的可追溯性和体系结构维护，并促进软件维护者对底层设计原理的可视化。拟议的技术将有可能影响各种高性能和安全关键软件系统。该项目做出了几个重要贡献。首先，它为将架构评估和痕迹检索领域的现有技术整合到维护过程中提供了一个潜在的变革性解决方案。其次，它开发了新的算法和技术，用于捕获和重用跨项目的可追溯性知识，其形式是语义类型的可追溯性链接的可重用基础设施。第三，它提供了新的可视化技术，用于在常见软件工程任务的上下文中向开发人员和维护人员传达底层架构决策及其相互关系。第四，利用轨迹检索、机器学习和结构分析技术的结合，提出了尖端的轨迹检索解决方案，以检测与建筑策略相关的类别，然后根据这些类别在策略中的特定角色对这些类别进行分类。最后，它利用这些信息自动生成架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.