Software Reuse

在创建软件系统时，开发人员试图避免为每个需要的功能重新发明轮子。因此，它们通常依赖于提供实现和构建块工具箱的第三方软件库。然而，并非所有的库都具有相同的质量、易用性或社区支持。既然有许多库可供选择，开发人员如何选择最适合他们需要的库呢？如果他们选择了一个库，但后来发现了一个更好的替代库，他们如何自动切换到新的库，而不必重新编写使用旧库已经实现的所有功能？Nadi博士的研究项目的目标是提供自动化的支持工具，帮助开发人员处理这种情况。具体来说，Nadi博士将开发一些指标，帮助开发人员推断重要的选择因素（例如，库文档的质量和易用性）。她还将设计自动化库迁移技术，特别是针对Python，帮助开发人员轻松地在库之间切换，而无需额外的手工劳动。上面描述的情况是任何开发代码的人都会遇到的情况。事实上，在处理库时，不再只有传统意义上的软件开发人员面临这样的情况；新兴的数据科学家群体现在在大多数领域都很普遍，他们也面临着同样的问题。然而，考虑到数据科学家的背景和培训的多样性（例如，社会科学或商业背景），驾驭这种情况可能比软件开发人员更棘手。因此，数据科学家及其高度专业化的库可能需要考虑其他因素和支持类型。作为拟议项目的最终方向，Dr. Nadi将调查数据科学家在库选择和使用方面的需求，并根据这些需求定制上述支持工具。利用定性研究、挖掘软件存储库、机器学习和程序分析的创新组合，Nadi博士为软件库用户提供了工具和技术，帮助他们在日常活动上花费更少的时间。与此同时，它为他们提供数据和解释，帮助他们完成需要他们专业知识的更具创造性的活动。总的来说，这种支持提高了开发复杂代码的效率和有效性。

When creating a software system, developers try to avoid re-inventing the wheel for every required functionality. Thus, they often rely on third-party software libraries that provide a toolbox of implementations and building blocks. However, not all libraries have the same quality, ease of use, or community support. Given many library alternatives to choose from, how can developers select the best library for their needs? What if they select a library but later find a better alternative, how can they automatically switch to the new library without having to re-write all the functionality already implemented using the old library? The goal of Dr. Nadi's research program is to provide automated support tools that help developers deal with such situations. Specifically, Dr. Nadi will develop metrics that help developers reason about important selection factors (e.g., library documentation quality and ease of use) . She will also design automated library migration techniques, especially for Python, that help developers easily switch between libraries without additional manual labor.The situations described above are ones that anyone developing a piece of code runs into. In fact, it is no longer only software developers in the traditional sense that face such situations when dealing with libraries; the new emerging group of data scientists that are now pervasive in most fields also face these same questions. However, given the diversity of background and training of data scientists (e.g., a social sciences or business background), navigating such situations may be even trickier than software developers. Thus, there may be additional factors and type of support to consider for data scientists and their highly specialized libraries. As the final direction of the proposed program, Dr. Nadi will investigate the needs of data scientists when it comes to library selection and usage, and customize the above support tools to these needs.Using innovative combinations of qualitative research, mining software repositories, machine learning, and program analysis, Dr. Nadi provides software library users with tools and techniques that help them spend less time on mundane activities. At the same time, it empowers them with data and explanations to help them accomplish more creative activities that need their expertise. Overall, such support improves both the efficiency and effectiveness with which complex code can be developed.