EAGER: Statistical Modeling of Linguistic Change in Open Source Software

该项目探索了一种基于统计自然语言处理技术的开源软件(OSS)演化理论。基于对软件代码在许多方面与自然语言(例如，英语)一样“自然”这一新兴认识，存在将统计模型应用于诸如代码分析、理解和程序员支持等软件开发任务的趋势。这项资助通过研究开放源码软件中的代码词典是如何随着不同的开发人员在一个软件项目上工作以及功能被添加、修改和删除而演变的，从而扩展了“代码的自然性”理论。目标是了解开发人员词典的演变在多大程度上遵循自然语言的演变规律。为了创建所需的演示，从大量OSS项目及其修订版(在GitHub和SourceForge上)收集了大量代码词典的数据集。自然语言演变频率模型的主要结构将用于跟踪和确定整个开放源码软件项目生命周期内语言变化的主要模式(例如，词典中术语的诞生、传播、消亡)。挑战的一部分是更好地理解引发代码演变的事件(如维护活动和团队组建)与引发自然语言变化的事件(如战争和迁移)有何根本不同。这项研究应该导致预测软件项目结果的新方法，并提高软件生产率和质量。该项目将提供该项目将产生的数据、工具和算法，这些数据、工具和算法将支持未来的工作，以了解开源软件生态系统中代码演变的动态。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The project explores a theory of open source software (OSS) evolution based on statistical natural language processing techniques. Based on the emerging recognition that software code is, in many ways, as "natural" as natural language (e.g., English), there is a trend to apply statistical models for software development tasks such as code analysis, comprehension, and programmer support. This grant extends the "naturalness of code" theory by studying how the code lexicon evolves in open source software as different developers work on a software project and features are added, modified, deleted. The goal is to learn the extent to which the evolution of a developer's lexicon follows the laws of natural language evolution.To create the needed demonstration, large datasets of code lexicons are being collected from a large number of OSS projects and their revisions (on GitHub and SourceForge). The main constructs of the frequency model of natural language evolution will be applied to track and identify the main patterns of language changes (e.g., birth, propagation, death of terms in the lexicon) throughout OSS projects life cycle. Part of the challenge is to better understand how events that instigate code evolution, such as maintenance activities and team formation, are fundamentally different from the events that instigate change in natural language, such as war and migration. The research should lead to new ways to predict software project outcomes and to improve software productivity and quality. The project will make available the data, tools, and algorithms that will be produced by the project, which will support future work to understand the dynamics of code evolution in open source software ecosystems.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.