SHF: Small: Collaborative: Managing Software Evolution through Continuous Measuring and Monitoring

架构退化会对软件质量和生产力产生根本性的影响，导致大量的时间和金钱损失。据估计，仅在美国，失败和有问题的软件每年就花费600亿至700亿美元，最近的研究表明，只有2%-6%的大型项目是成功的。这些问题都不是一夜之间发生的。软件通过许多维护任务不断发展，例如修复错误和添加新功能。开发人员通常专注于他们的直接任务，而他们的活动的累积影响，因为他们影响架构，而不被注意。早期症状不断发展，范围和意义不断增长，直到系统变得难以维护。已经提出了许多软件度量和措施来监控软件的可维护性，但它们还没有达到比较和对比项目所需的可靠性，或信号严重问题的早期症状。目前的技术，主要是基于（静态）语法依赖性，往往会报告大量的误报。这使得很难找出真正的问题。该项目将开发一个新的度量套件的基础上的选项理论的源代码评估，伴随着一套进化的历史措施，使用维护任务作为一级实体。该项目还将开发一种热点检测方法，以在软件变得不可管理的bug和昂贵的修复之前捕获架构缺陷的早期症状。 该项目有可能彻底改变软件系统的监控和管理方式，可能为开发组织节省大量资金，并导致软件演化和维护的过程更加规范，可控。拟议的指标和工具将与广泛使用的软件管理工具（如Bitbucket和Github）集成，可能会影响数十万个软件项目。工业基准将建立一个软件健康图，任何软件项目都可以与之进行比较。本研究所提出的方法可应用于软件设计与维护教学中，为软件设计与维护教学提供科学依据。 这项研究的经验基础将为软件进化和管理的推理提供一个全行业的基础。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

Architecture degradation can have fundamental impacts on software quality and productivity, resulting in substantial loss of time and money. It has been estimated that failed and troubled software costs around 60 billion to 70 billion dollars per year in the United States alone, and recent research has revealed that only 2%-6% of large projects are successful. None of these problems happen overnight. Software evolves continuously, through numerous maintenance tasks, such as fixing bugs and adding new features. Developers typically are focused on their immediate tasks while the cumulative impacts of their activities, as they affect the architecture, go unnoticed. Early symptoms continuously evolve and grow in scope and significance until the system becomes difficult to maintain. Numerous software metrics and measures have been proposed to monitor software maintainability, but they have not yet achieved the reliability needed for comparing and contrasting projects, or to signal early symptoms of severe problems. Current techniques, which are largely based on (static) syntactic dependencies, tend to report large numbers of false positives. This makes it hard to pinpoint the true problems. The project will develop a novel metric suite for source code assessment based on options theory, accompanied by a suite of evolution history measures using maintenance tasks as first-class entities. The project will also develop a hotspot detection method to capture early symptoms of architecture flaws before the software becomes unmanageably buggy and costly to repair. This project has the potential to revolutionize how software systems are monitored and managed, potentially resulting in substantial savings for development organizations, and resulting in a more disciplined, controlled process of software evolution and maintenance. The proposed metrics and tools will be integrated with widely-used software management tools, such as Bitbucket and Github, potentially impacting hundreds of thousands of software projects. The industrial benchmark will establish a Software Heath Chart against which any software project can make a comparison. The methods produced from this research can be used in software design and maintenance education, providing pedagogical tools with scientific foundations. The empirical basis for this research will provide an industry-wide foundation for reasoning about software evolution and management.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.