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源代码[BNPMedia，2020]。开源软件的主要好处是能够访问、修改和使用源代码;这意味着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着关键作用，但也并非没有缺点。由于开放源码软件可通过各种手段获取，因此可能被使用和滥用。开放源码软件从业人员可能会遇到安全风险、容易出错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bug来源对bug预测和本地化有显著的负面影响，并且OSS贡献的评估可能不会“自由”系统偏差。我的长期目标是开发技术，以确保为不同的用户开发高质量，有效，高效和公正的OSS系统，而不会浪费资源或增加成本。在这一发现补助金（DG）研究计划的范围内，将通过三个子目标（SO）实现这一目标的进展：（SO 1）研究新技术，以预测和定位在OSS开发过程中的内在和外在错误;（SO 2）通过理解和描述外在错误来改进OSS开发实践;（SO 3）设计一个OSS框架，用于开发公正和公平的OSS系统。这三个子目标将在我的监督下由3名博士，3名硕士和1名本科生HQP领导。我将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。 次级目标将采用混合方法研究方法来实现。在高层次上，我的目标是对OSS开发观察数据采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上托管的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，以衡量在计划期间开发的技术和工具的有效性。此外，我将使用纵向现场研究来了解所提出的工具/技术在帮助软件工程师提高软件质量和相应地调整工具行为方面的有效性。 这一总干事的成果对加拿大这样一个多文化社会特别有益，因为加拿大人可以走在开放源码软件开发界的前列，因此越来越希望在技术开发中发挥重要作用。国家科学和工程界的新奇在于所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.