CAREER: Informed Decision Making for Software Change

职业：软件变更的知情决策

• 批准号: 2239107
• 负责人: Oscar Javier Chaparro Arenas
• 金额: $ 55.01万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239107&HistoricalAwards=false
• 关键词: CAREER; Informed; Decision; Making; Software

Software engineers continuously change source code to add new functionality and make improvements to software systems. Unfortunately, during this process, engineers often make code changes that increase software defects, unintended system behavior, and code deterioration. Low-quality code changes have significant consequences for end-users, organizations, and society, as they create excessive costs in software development, operation, and maintenance. In fact, in 2020, the estimated cost of low-quality software in the United States was two trillion dollars. A crucial cause for low-quality code changes is the fact that software engineers often have insufficient knowledge about the code, which leads them to make poor decisions on how to correctly change it. Obtaining such knowledge is extremely challenging, in part because documented code-related information is unstructured, fragmented, and scattered across various software artifacts/repositories without explicit traceability relationships among them. To address these fundamental challenges, this project will process and manage code change decisions documented in software artifacts/repositories to assist engineers in making informed decisions on how to correctly change source code. The premise that guides this project, supported by prior research results, is that documented code change decisions contain valuable code-related knowledge that can inform software engineers in designing and implementing code changes that meet software requirements and minimize the introduction of defects, unintended system behavior, and code deterioration. This project will produce a novel theory of decision-making for code change, as well as novel techniques and interactive/integrated tool support to better capture, trace, and recommend code change decision information, useful to design and implement high-quality code changes. The results of this project will allow software engineers to easily capture and manage their decisions in software artifacts while they are solving new feature/enhancement requests and defect reports. Additionally, engineers will better learn from prior decisions to produce software that is less faulty and easier to maintain. Organizations and society will benefit from software systems that support their business processes more effectively, leading to lower costs in software development, operation, and maintenance. This project is centered on three goals. First, it will develop a theory of code change decisions that will document: (i) strategies and patterns of code change decision-making, (ii) factors that make adequate and poor code change decisions, and (iii) actionable guidelines on how software engineers should make/reuse code change decisions to solve new problems. Second, it will design and develop novel automated techniques and interactive tool support for capturing and tracing information elements of code change decisions, while engineers document code-related knowledge in various software artifacts. Third, it will design and develop novel automated techniques and interactive tool support to inform engineers about: (i) the reasons why past code changes were made, (ii) past code change decisions relevant to solve a new feature/enhance request or defect report, and (iii) evidence of the impact that past decisions had on code quality and defect introduction. The proposed theory and techniques will be developed through cross-cutting research on empirical software engineering, automated text analysis, machine/deep learning, information retrieval, and human-computer interaction. The project aims to educate the next generation of software engineers with a strong foundation and skills to build and evolve high-quality software. Students at all scholarly levels will learn about code change decisions and the way these can be used to effectively build and maintain software. The project will create reusable educational course packages, integrate research tools into course projects, organize community-building and outreach events, and recruit/retain students from underrepresented groups.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.

软件工程师不断更改源代码以添加新功能并改进软件系统。不幸的是，在此过程中，工程师经常进行代码更改，以增加软件缺陷，意外的系统行为和代码劣化。低质量的代码更改对最终用户，组织和社会产生重大影响，因为它们在软件开发，操作和维护方面创造了过高的成本。实际上，在2020年，美国低质量软件的估计成本为2万亿美元。低质量代码更改的关键原因是，软件工程师通常对代码知识不足，这使他们对如何正确更改它做出了不良的决定。获得此类知识非常具有挑战性，部分原因是有记录的与代码相关的信息是非结构化的，分散的，并且散布在各种软件文物/存储库中，而没有明确的可追溯性关系。 为了应对这些基本挑战，该项目将处理和管理软件工件/存储库中记录的代码更改决策，以帮助工程师对如何正确更改源代码做出明智的决定。在先前的研究结果的支持下，指导该项目的前提是，已记录的代码更改决策包含有价值的代码相关知识，这些知识可以告知软件工程师在设计和实施符合软件要求的代码更改时，并最大程度地减少了缺陷，意外的系统行为和代码降低。该项目将为代码更改提供新颖的决策理论，以及新颖的技术和交互式/集成的工具支持，以更好地捕获，跟踪和推荐代码更改决策信息，可用于设计和实施高质量的代码更改。该项目的结果将使软件工程师在解决新功能/增强请求和缺陷报告时轻松捕获和管理其在软件工件中的决策。此外，工程师将更好地从先前的决策中学习，以制作出较不错且易于维护的软件。组织和社会将从软件系统中受益，这些软件系统可以更有效地支持其业务流程，从而降低软件开发，操作和维护的成本。该项目以三个目标为中心。首先，它将制定一个代码更改决策理论，该理论将记录：（i）代码更改决策制定的策略和模式，（ii）使适当和差的代码更改决策的因素以及（iii）有关软件工程师如何制定/重复使用代码更改决策以解决新问题的可行指南。其次，它将设计和开发新颖的自动化技术和交互式工具支持，以捕获和追踪代码更改决策的信息元素，而工程师在各种软件文物中进行了文档记录与代码相关的知识。第三，它将设计和开发新颖的自动化技术和交互式工具支持，以告知工程师：（i）对过去代码进行更改的原因，（ii）与解决新功能/增强请求或缺陷报告相关的过去代码更改决策，以及（iii）过去决策对代码质量和缺陷简介的影响的证据。提出的理论和技术将通过对经验软件工程，自动化文本分析，机器/深度学习，信息检索和人类计算机相互作用进行的横切研究来开发。该项目旨在教育下一代软件工程师的基础和技能，以构建和发展高质量的软件。学生在学术层面上的所有学术层面都将了解代码更改决策，以及这些可以使用这些决定的方式来有效地构建和维护软件。该项目将创建可重复使用的教育课程套餐，将研究工具整合到课程项目中，组织社区建设和推广活动，并招募/保留来自代表性不足的小组的学生。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来获得支持的审查标准。