EAGER: Statistical Modeling of Linguistic Change in Open Source Software

EAGER：开源软件语言变化的统计建模

• 批准号: 1821525
• 负责人: Anas Mahmoud
• 金额: $ 6.31万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821525&HistoricalAwards=false
• 关键词: EAGER; Statistical; Modeling; Linguistic; Change

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.

该项目探索了基于统计自然语言处理技术的开源软件（OSS）进化理论。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”的认识，有一种趋势是将统计模型应用于软件开发任务，例如代码分析、理解和程序员支持。这项资助扩展了“代码的自然性”理论，通过研究代码词典在开源软件中如何随着不同的开发人员在一个软件项目中工作以及特性的添加、修改和删除而演变。目标是了解开发人员的词汇的演变在多大程度上遵循自然语言演变的规律。为了创建所需的演示，从大量的OSS项目及其修订（在GitHub和SourceForge上）收集了大量的代码词典数据集。自然语言进化频率模型的主要构造将用于跟踪和识别整个OSS项目生命周期中语言变化的主要模式（例如，词汇中术语的诞生、传播和消亡）。挑战的一部分是更好地理解引发代码进化的事件（例如维护活动和团队组建）与引发自然语言变化的事件（例如战争和迁移）是如何从根本上不同的。这项研究应该会导致新的方法来预测软件项目的结果，并提高软件的生产力和质量。该项目将提供由该项目产生的数据、工具和算法，这将支持未来的工作，以了解开源软件生态系统中代码演变的动态。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Linguistic Documentation of Software History

软件历史的语言文档

• DOI: 10.1145/3387904.3389288
• 发表时间: 2020
• 期刊: the 28th International Conference on Program Comprehension
• 作者: Tushev, M;Mahmoud, A.
• 通讯作者: Mahmoud, A.

On Combining IR Methods to Improve Bug localization

• DOI: 10.1145/3387904.3389280
• 发表时间: 2020-07
• 期刊: 2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)
• 作者: Saket Khatiwada;Miroslav Tushev;Anas Mahmoud

Using GitHub in large software engineering classes. An exploratory case study

在大型软件工程课程中使用 GitHub。

• DOI: 10.1080/08993408.2019.1696168
• 发表时间: 2019
• 期刊: Computer Science Education
• 影响因子: 2.7
• 作者: Tushev, Miroslav;Williams, Grant;Mahmoud, Anas
• 通讯作者: Mahmoud, Anas

Linguistic Change in Open Source Software

开源软件的语言变化

• DOI: 10.1109/icsme.2019.00045
• 发表时间: 2019
• 期刊: 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME
• 作者: Tushev, Miroslav;Khatiwada, Saket;Mahmoud, Anas
• 通讯作者: Mahmoud, Anas