SHF:Small: Mega-Transfer: On the Value of Learning from 10,000+ Software Projects

随着越来越多的软件和软件开发工件在大型存储库中积累，软件工程已经成为一门大数据科学。 软件分析是软件工程的一个子领域，其中分析软件项目数据以获得可操作的信息，帮助从业者跟踪和改进软件开发实践。软件分析的研究旨在开发更好的模型，方法和工具，以支持例如使用软件生产和程序员活动数据的软件缺陷预测。软件分析的第一个十年已经产生了关于特定项目的特定结果，但仍然很难在许多项目中进行概括，以便将一个项目的结果应用于其他项目。 与任何大数据科学一样，一个核心问题是需要多少数据以及什么质量的数据才能得出有效和有用的结论，这些结论可以在多个项目中推广。 软件分析领域的一个典型的研究项目不超过几十个，这限制了该领域结果的有效性和实用性。该项目将探索跨领域迁移学习数据的充分性和局限性。 最终目标是扩展软件分析，特别是将九个广泛研究的软件分析任务的学习转移到10，000多个开源项目和1，500多个商业项目。其结果是软件质量预测模型，由大规模的定量研究，可以指导软件的开发和维护，从而降低创建和维护软件的总成本。这项工作的技术挑战包括算法的可扩展性和在项目之间转移经验教训的能力。为了应对这些挑战，该项目将开发基于快速线性时间聚类算法和快速流挖掘算法的创新迁移学习方法，这些算法使用增量超参数优化和聚类。 该技术将在实例空间（每个集群内）中找到类似的项目，以便找到转移的候选项目。此外，在可能的情况下，该项目将通过对源数据和目标数据进行特征选择的预先研究来降低传输成本。如果发现软件知识高度本地化，则该项目还将为大规模基于实例的学习方法开发和部署优化方法。该奖项反映了NSF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。

As more and more software and software-development artifacts have accumulated in large repositories, Software Engineering has become a Big Data Science. Software analytics is a subfield of Software Engineering in which the software-project data is analyzed to gain actionable information that helps practitioners track and improve software-development practices. Research in software analytics seeks to develop better models, methods and tools to support, for example, software-defect prediction, using software production and programmer activity data. The first decade of software analytics has generated specific results about specific projects, but it remains difficult to generalize across many projects so that results on one project can be applied to other projects. As in any big-data science, a central question is how much data, and of what quality, is needed to draw valid and useful conclusions that can generalize across multiple projects. A typical research study in software analytics looks at no more than a few dozen projects, which limits the validity and usefulness of results in the field. The project will explore the sufficiency and limits of data for transfer learning across domains. The ultimate goal is to scale software analytics, particularly transfer learning of nine extensively studied software-analytics tasks, to 10,000+ open-source projects and 1,500+ commercial projects. The result is software-quality prediction models, backed by large-scale quantitative studies, that can guide the development and maintenance of software, thus reducing the overall cost of creating and maintaining software.The technical challenges of this work include scalability of the algorithms and the ability to transfer lessons learned between projects. To address these challenges this project will develop innovative transfer-learning methods based on fast linear-time clustering algorithms and fast stream-mining algorithms that use incremental hyper-parameter optimization and clustering. The techniques will find similar projects in instance space (within each cluster) in order to find candidates for transfer. Further, where possible, this project will reduce the transfer cost by pre-studies that perform feature selection on the source and target data. Where it is found that software knowledge is highly localized, then this project will also develop and deploy optimization methods for large scale instance-based learning methods.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.