SHF: Medium: Collaborative Research: Finding and Fixing Architectural Hotspots: An Economics-Based Decision Support Approach

最近的研究表明，源代码文件中的错误倾向和更改倾向与软件体系结构决策之间存在很强的相关性。也就是说，即使软件系统可能有数百个有错误的文件，这些文件始终只形成几个在体系结构上相连的组：体系结构热点。热点表现出在源文件之间传播错误的体系结构缺陷。这种现象已经在许多项目中观察到，无论是开放源码项目还是工业项目，无论其领域、年龄或编程语言如何。这意味着，在不修复导致这些错误传播的体系结构问题的情况下，不可能降低复杂软件系统中的错误或更改率。这里提出的研究目标是指导高维护体系结构问题的识别，从软件质量和生产力的角度定量描述其后果，并创建业务案例来证明其重构的合理性。最终目标是通过战略架构改进来降低长期的软件维护成本。这项研究的关键是自动提取架构热点，并根据增加的错误修复工作或降低的功能交付能力来量化它们的经济后果。这种量化涉及构建利用软件项目中广泛可用的信息的模型--关于错误、更改和提交--以便架构师可以计划对热点的重构，并自信地估计这种重构的成本和收益。这项研究将通过私人投资机构广泛的国内和国际学术和产业合作产生直接影响。它将从根本上改变发现、检查和处理软件缺陷的方式：分析师只需检查建议方法检测到的几个体系结构热点，而不是单独检查数百个有缺陷的文件，通过移除体系结构根源同时修复多个缺陷，从而大幅节省维护成本。拟议的决策支持方法通过为软件体系结构决策的定价和风险分析提供经验基础，有可能改变软件行业的管理。提出的体系结构热点检测方法将影响众多软件工程研究领域，为软件体系结构和设计分析教学提供工具支持，将对软件设计教育产生重大影响。

Recent research has revealed strong correlations between error-proneness and change-proneness in source code files and software architecture decisions. That is, even though a software system may have hundreds of buggy files, these files always form just a few architecturally connected groups: architecture hotspots. Hotspots exhibit architectural flaws that propagate errors among source files. This phenomenon has been observed over numerous projects, both open source and industrial, regardless of their domain, age, or programming language. The implication is that it is impossible to reduce error or change rates in complex software systems without fixing the architecture problems that cause these errors to propagate. The objective of the research proposed here is to guide the identification of high-maintenance architecture problems, quantitatively characterize their consequences in terms of software quality and productivity, and create business cases to justify their refactoring. The end goal is to reduce long-term software maintenance costs though strategic architecture improvement.The key to this research is to automatically extract architecture hotspots, and to quantify their economic consequences in terms of increased bug-fixing effort or reduced ability to deliver features. This quantification involves building models that leverage information broadly available in software projects--on bugs, changes, and commits--so that an architect can plan refactorings to the hotspots and confidently estimate the costs and benefits of such refactorings. This research will produce direct impact through the PIs' extensive national and international academic and industrial collaborations. It will fundamentally change how software defects are discovered, examined, and handled: instead of examining hundreds of defective files, each one in isolation, the analyst only needs to examine a few architecture hotspots detected by the proposed approach, fixing numerous defects simultaneously by removing their architecture roots, thus providing substantial savings in maintenance costs. The proposed decision-support approach has the potential to change the management of the software industry by providing an empirical basis for the pricing and risk analysis of software architecture decisions. The proposed architecture hotspot detection approach will influence numerous software engineering research areas, and will have significant impact on software design education by providing tool-support for the teaching of software architecture and design analysis.