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

推进新方法来改进在线协作编码平台中软件开发的实践和流程

• 批准号: RGPIN-2022-03265
• 负责人: RodriguezPerez, Gema
• 金额: $ 1.82万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761693
• 关键词: Advancing; Novel; Approaches; Improve; Practices

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.

用于软件开发的绝大多数（高达 99%）代码库都包含开源代码 [BNPMedia，2020]。开源软件 (OSS) 的主要优点是能够访问、修改和使用源代码；这意味着理论上任何有能力的用户都可以参与源代码的开发。这提高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 OSS 可以通过各种方式访问​​，因此它可能会被使用和滥用。 OSS 从业者可能会遇到安全风险、容易出现错误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定位具有重大的负面影响，并且对 OSS 贡献的评估可能不会“摆脱”系统偏差。我的长期目标是开发技术，确保为不同的用户开发高质量、有效、高效和公正的 OSS 系统，而不浪费资源或增加成本。在该发现资助 (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： (SO1) 研究预测和定位 OSS 开发过程中内在和外在错误的新技术； (SO2) 通过理解和表征外在错误来改进 OSS 开发实践； (SO3) 设计一个 OSS 框架，用于开发公正且公平的 OSS 系统。这三个子目标将由 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，并从整体角度考虑HQP的申请。 子目标将使用混合方法研究方法来实现。在高层次上，我的目标是对 OSS 开发观测数据采用定量统计分析和各种基于深度学习、机器学习和自然语言处理技术。我将使用挖掘软件存储库方法从在线协作编码平台托管的 OSS 存储库收集 OSS 开发数据。我还将使用定性研究方法，例如访谈和调查，来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并相应调整工具行为方面的有效性。 该总干事的成果对于像加拿大这样的多元文化社会特别有利，加拿大越来越希望在技术开发中发挥重要作用，因为加拿大人可以在 OSS 开发社区的最前沿发挥作用。国家科学与工程界的新颖之处在于所开发的技术和工具将有助于提高 OSS 系统的质量。