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

SHF：小型：协作：通过持续测量和监控管理软件演化

• 批准号: 1817267
• 负责人: Rick Kazman
• 金额: $ 20.12万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817267&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Managing; Software

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.

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的成本约为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。通过大量的维护任务，例如修复错误和添加新功能，软件不断发展。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，因为它们影响了体系结构，被忽视了。早期症状在范围和重要性上不断发展和增长，直到系统变得难以维持。已经提出了许多软件度量和度量来监视软件的可维护性，但是它们还没有达到比较和对比项目所需的可靠性，或者表明严重问题的早期症状。当前的技术很大程度上基于（静态）语法依赖，往往会报告大量的误报。这使得很难找出真正的问题。该项目将基于期权理论为源代码评估开发一套新的度量标准，并附带一套使用维护任务作为一级实体的进化历史度量标准。该项目还将开发一种热点检测方法，在软件出现难以管理的漏洞和修复成本高昂之前，捕捉架构缺陷的早期症状。这个项目有可能彻底改变软件系统的监控和管理方式，潜在地为开发组织节省大量资金，并产生一个更有纪律的、受控制的软件演进和维护过程。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。本研究所产生的方法可用于软件设计和维护教育，为教学工具提供科学的基础。本研究的实证基础将为软件演进和管理的推理提供一个全行业的基础。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

项目成果

Detecting the Locations and Predicting the Maintenance Costs of Compound Architectural Debts

• DOI: 10.1109/tse.2021.3102221
• 发表时间: 2021-08
• 期刊: IEEE Transactions on Software Engineering
• 影响因子: 7.4
• 作者: Lu Xiao;Yuanfang Cai;R. Kazman;Ran Mo;Qiong Feng

A Longitudinal Study of Identifying and Paying Down Architecture Debt

• DOI: 10.1109/icse-seip.2019.00026
• 发表时间: 2018-11
• 期刊: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)
• 作者: Maleknaz Nayebi;Yuanfang Cai;R. Kazman;G. Ruhe;Qiong Feng;Chris Carlson;Francis Chew

CIDER: concept-based interactive design recovery

CIDER：基于概念的交互设计恢复

• DOI: 10.1145/3510454.3516861
• 发表时间: 2022
• 期刊: 2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion
• 作者: Fang, Hongzhou;Cai, Yuanfang;Kazman, Rick;Lefever, Jason
• 通讯作者: Lefever, Jason

On the Lack of Consensus Among Technical Debt Detection Tools

论技术债务检测工具缺乏共识

• DOI: 10.1109/icse-seip52600.2021.00021
• 发表时间: 2021
• 期刊: 10.1109/ICSE-SEIP52600.2021.00021
• 作者: Lefever, Jason;Cai, Yuanfang;Cervantes, Humberto;Kazman, Rick;Fang, Hongzhou
• 通讯作者: Fang, Hongzhou