SHF: MEDIUM: Collaborative Research: Transfer Learning in Software Engineering

SHF：中：协作研究：软件工程中的迁移学习

• 批准号: 1302169
• 负责人: Lucas Layman
• 金额: $ 48.29万
• 依托单位: Fraunhofer Center for Experimental Software Engineering
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302169&HistoricalAwards=false
• 关键词: SHF; MEDIUM; Collaborative; Research; Transfer

The goal of the research is to enable software engineers to find software development best practices from past empirical data. The increasing availability of software development project data, plus new machine learning techniques, make it possible for researchers to study the generalizability of results across projects using the concept of transfer learning. Using data from real software projects, the project will determine and validate best practices in three areas: predicting software development effort; isolating software detects; effective code inspection practices. This research will deliver new data mining technologies in the form of transfer learning techniques and tools that overcome current limitations in the state-of-the-art to provide accurate learning within and across projects. It will design new empirical studies, which apply transfer learning to empirical data collected from industrial software projects. It will build an on-line model analysis service, making the techniques and tools available to other researchers who are investigating validity of principles for best practice. The broader impacts of the research will be to make empirical software engineering research results more transferable to practice, and to improve the research processes for the empirical software engineering community. By providing a means to test principles about software development, this work stands to transform empirical software engineering research and enable software managers to rely on scientifically obtained facts and conclusions rather than anecdotal evidence and one-off studies. Given the immense importance and cost of software in commercial and critical systems, the research has long-term economic impacts.

研究的目标是使软件工程师能够从过去的经验数据中找到软件开发的最佳实践。软件开发项目数据的可用性越来越高，再加上新的机器学习技术，使研究人员能够使用迁移学习的概念来研究跨项目结果的可推广性。使用来自真实的软件项目的数据，该项目将确定和验证三个领域的最佳实践：预测软件开发工作量;隔离软件检测;有效的代码检查实践。这项研究将以迁移学习技术和工具的形式提供新的数据挖掘技术，这些技术和工具克服了当前最先进技术的局限性，从而在项目内和项目间提供准确的学习。它将设计新的实证研究，将迁移学习应用于从工业软件项目收集的实证数据。它将建立一个在线模型分析服务，使技术和工具提供给其他研究人员谁是调查的有效性原则的最佳做法。更广泛的影响，研究将使实证软件工程研究成果更可转移到实践中，并改善实证软件工程社区的研究过程。 通过提供一种方法来测试软件开发的原则，这项工作将改变实证软件工程研究，使软件管理人员能够依靠科学获得的事实和结论，而不是轶事证据和一次性的研究。鉴于软件在商业和关键系统中的巨大重要性和成本，该研究具有长期的经济影响。