Advancing Novel Approaches to Improve the Practices and Processes of Software Development in Online Collaborative Coding Platforms

绝大多数(高达99%)用于软件开发的代码库包含开放源代码[BNPMedia，2020]。开源软件(OSS)的主要好处是能够访问、修改和使用源代码；这意味着在理论上任何有能力的用户都能够参与源代码的开发。这增加了软件的整体创新和创造力，并在高科技公司和其他行业的商业模式中发挥了关键作用，但也不是没有缺点。由于开放源码软件可以通过各种方式获取，因此它可能被使用和误用。OSS从业者可能会面临安全风险、错误倾向以及缺乏用户支持和专业知识。值得注意的是，我和我的合作者最近表明，以前未考虑的错误起源对错误预测和本地化有重大的负面影响，而且对OSS贡献的评估可能不是没有系统偏见。我的长期目标是开发技术，确保在不浪费资源或增加成本的情况下，为不同的用户开发高质量、有效、高效和不偏不倚的OSS系统。在Discovery Grant(DG)研究计划的范围内，将通过三个子目标(SO1)实现这一目标：(SO1)研究新技术，以预测和定位开放源码软件开发过程中的内在和外在错误；(SO2)通过了解和表征外在错误来改进开放源码软件开发实践；(SO3)设计一个开放源码软件框架，用于开发公正和公平的开放源码软件系统。这三个分目标将由3名博士、3名硕士和1名本科生HQP在我的监督下领导。我会积极从历史上被边缘化的群体中招募HQP，并从整体的角度考虑HQP的申请。这些分目标将使用混合方法研究方法来实现。在高层次上，我的目标是在OSS开发观测数据上使用定量统计分析和各种基于深度学习、基于机器学习和自然语言处理的技术。我将使用挖掘软件存储库的方法从在线协作编码平台托管的OSS存储库中收集OSS开发数据。我还将使用定性研究方法，例如访谈和调查，以衡量项目期间开发的技术和工具的有效性。此外，我将使用纵向实地研究来了解所建议的工具/技术在帮助软件工程师改进软件质量和相应地调整工具的行为方面的有效性。这一成果对加拿大这样的多元文化社会尤其有利，因为加拿大人可以走在开放源码软件开发社区的前列，因此越来越希望在技术发展中发挥相当大的作用。国家科学和工程界的新奇之处在于开发的技术和工具将有助于提高开放源码软件系统的质量。

The vast majority (as much as 99%) of codebases used for software development contain open source code [BNPMedia, 2020]. Key benefits of Open Source Software (OSS) are the ability to access, modify, and use the source code; meaning that in theory any capable user is able to participate in development of source code. This has increased the overall innovation and creativity of software and plays a critical role in the business models for high-tech firms and other industries, but is not without its drawbacks. As OSS is accessible to all means, it can be used and misused. OSS practitioners can expect security risks, bug proneness, and lack of user support and expertise. Notably, my collaborators and I have recently shown that previous unconsidered bug origins have a significant, negative impact on bug prediction and localization and that the evaluation of OSS contributions might not be "free" of sytemic bias. My long-term objective is to develop techniques to ensure that high-quality, effective, efficient, and unbiased OSS systems are developed for diverse users without wasting resources or increasing costs. Within the scope of this Discovery Grant (DG) research program, progress towards this goal will be reached through three sub-objectives (SO): (SO1) Studying novel techniques to predict and locate intrinsic and extrinsic bugs during OSS development; (SO2) Improving OSS development practices by understanding and characterizing extrinsic bugs; (SO3) Devising an OSS framework for developing unbiased and fair OSS systems. These three sub-objectives will be led by 3 PhD, 3 MSc and 1 Undergrad HQP under my supervision. I will actively recruit HQP from historically marginalized groups and consider HQP applications from a holistic perspective. The sub-objectives will be accomplished using mixed-methods research approaches. At a high level, I aim to employ quantitative statistical analyses and a variety of deep learning-based, machine learning-based, and natural language processing techniques on OSS development observational data. I will use mining software repository approaches to gather the OSS development data from OSS repositories hosted in online collaborative coding platforms. I will also use qualitative research methods, e.g., interviews and surveys, to gauge the effectiveness of the techniques and tools developed during the program. In addition, I will use longitudinal field studies to understand how effective the proposed tools/techniques are to help software engineers improve software quality and adjust the behavior of the tools accordingly. The outcome of this DG is particularly beneficial to a multi-cultural society like Canada that is increasingly looking to play a considerable role in technology development as canadians can function in the forefront of the OSS development community. The novelty for the National Science and Engineering Community lies in the techniques and tools developed that will help improve quality of OSS systems.