CAREER: iProvenance: Integrating Data Provenance with Software Traceability

科学研究越来越多地由数据驱动--塑造我们对世界的理解的假设、模拟和模型都是基于观察到的数据而形成的。为了从这些观察中做出有效的推论，研究人员发现，他们还需要了解数据的来源，这是数据形成的来源和背景。为了处理不断增长的数据量，科学家们依赖于许多不同的软件程序(包括自主开发的和商业的)来收集、处理和分析数据。虽然电子科学界已经在支持数据来源方面取得了很大进展，但理解操纵数据的软件是一个在很大程度上尚未开发的关键研究领域。同时，在软件工程中，与来源相关的概念软件可跟踪性在理解源代码方面发挥着关键作用。可跟踪性使工程师能够将源代码链接到各种文档，以回答诸如“为什么使用此代码？”之类的问题。以及“在代码上执行了哪些测试？”软件可追溯性还有助于其他开发任务，如调试、确定软件质量和确定更改的影响。这个职业奖项目通过使用概念性框架iProvenance来关注eScience中数据和软件之间的关键相互作用，iProvenance将数据来源与软件可追溯性相结合，并以对来源挑战的全面检查为基础。IProvenance提供了同时建模和捕获以软件为中心和以数据为中心的信息的能力，创建了来源方法，从整体上解决了类似于软件可追溯性方面的挑战，并为不同领域的科学家开发了功能强大但可访问的自动来源软件。该方法对从事电子科学和数据来源的每个科学领域都是有益的。这项研究的结果还有可能改变拥有大量数字记录的行业的来源管理，包括电子健康记录和临床试验。最后，与研究活动相结合的教育活动不仅将使未来的计算机科学专业人员掌握数据来源技术和方法方面的知识，而且这些活动还将提供可在其他机构使用的课程和教育材料。

Scientific research is increasingly data-driven -- hypotheses, simulations, and models, which shape our understanding of the world, are formulated based on observed data. In order to make valid inferences from these observations, researchers are finding that they also need to understand data provenance, which is the origin and context of formation of the data. To tackle the increasing amounts of data, scientists rely on many different software programs (both homegrown and commercial) to collect, process, and analyze data. While the eScience community has made strides toward supporting data provenance, understanding the software that manipulates the data is a key research area that has largely been unexplored.Meanwhile, in software engineering, a concept related to provenance known as software traceability plays a key role in understanding source code. Traceability enables engineers to link source code to various pieces of documentation to answer questions such as "Why is this code used?" and "What tests were executed on the code?" Software traceability also facilitates other development tasks, such as debugging, determining software quality, and ascertaining the impact of a change.This CAREER Award project focuses on the crucial interplay between data and software in eScience by using a conceptual framework, iProvenance, that integrates data provenance with software traceability and is grounded in a holistic examination of provenance challenges. iProvenance provides the ability to model and capture software-centric and data-centric information in tandem, create provenance methods that holistically address challenges similar to those in software traceability, and develop powerful yet accessible automated provenance software for scientists in various domains.The approach is beneficial to every scientific field that engages in eScience and data provenance. The results of the research also have the potential to transform the management of provenance in industries with large amounts of digital records, including electronic health records and clinical trials. Finally, the educational activities interwoven with research activities will not only equip future computer science professionals with knowledge in data provenance techniques and methods, but these activities will also provide curriculum and educational materials that can be used at other institutions.