Intelligent Code Quality Management for Software Evolution

软件演进的智能代码质量管理

• 批准号: RGPIN-2022-03394
• 负责人: Zou, Ying
• 金额: $ 3.5万
• 依托单位: Queen's University
• 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=752724
• 关键词: Intelligent; Code; Quality; Management; Software

Software applications are omnipresent in our daily lives. We rely on them to pay our bills, to shop, and to stream videos online. Their quality is critical and cannot be compromised by their ever-increasing user base and programming complexity. To ensure the quality of software applications, software analytics are provided to automatically analyze the collected software engineering data (e.g., commit logs) and build predictive models to help developers identify defects in advance and improve software quality. However, as many large-scale and complex applications are deployed, existing software analytics approaches have the following limitations: (1) lack of industrial adaption as prior research cannot scale well to handle the increasing volume of software operational data (e.g., crash reports); and (2) rare considerations of the perceived quality of software experienced by end-users and treat all defects equal. In the proposed research, we aim to address the above limitations in the following objectives. O1) Providing intelligent support for handling the increasing scale of the code to deliver high quality software. We will develop leading-edge methods and tools to O1.1) provide smart clone management infrastructure that can track clone evolution and rank the clones based on various criteria (e.g., the fault-proneness) that require more attention; O1.2) extract reusable components as application programming interfaces (APIs) to reuse the functionality that are often embedded in the cloned code; and O1.3) identify and recommend API usage patterns to demonstrate how to implement a certain functionality. O2) Improving perceived quality of software by identifying the most impactful defects. Given the large and wide user base of software applications, new quality improvement processes and methodologies are needed ensuring that the developed software is of high quality. We will: O2.1) propose the notion of perceived quality (i.e., user experience and satisfaction) to identify high impact defects to fix first; and O2.2) optimize timing of pre-release efforts to ensure that software is released in a timely fashion, but the high impact defects are captured during the testing period. The proposed research focus is the development of solutions that are tested through empirical studies in an industrial setting. Due to the practical impact of my research, private sector companies (e.g., IBM) and open-source communities (e.g., the Mozilla Foundation) have shown strong interest in adopting our research results. The outcome of the proposed research will help practitioners deliver reliable and desirable software in an efficient manner. Integrating perceived quality into the evolution process help practitioners focus on the critical issues that can quickly improve perceived quality, thereby increasing customer satisfaction, brand reputation and ultimately company revenues. The proposed research will train 3 PhD and 2 MSc students.

软件应用在我们的日常生活中无处不在。我们依靠它们来支付账单、购物和在线播放视频。它们的质量至关重要，不能因其不断增长的用户群和编程复杂性而受到影响。为了保证软件应用的质量，提供软件分析功能，自动分析收集到的软件工程数据（例如提交日志）并构建预测模型，帮助开发人员提前识别缺陷并提高软件质量。然而，随着许多大规模和复杂的应用程序的部署，现有的软件分析方法具有以下局限性：（1）缺乏行业适应性，因为先前的研究无法很好地扩展以处理不断增加的软件操作数据量（例如崩溃报告） ; (2) 很少考虑最终用户体验到的软件质量，并对所有缺陷一视同仁。 在拟议的研究中，我们旨在通过以下目标解决上述局限性。 O1) 为处理不断增长的代码规模提供智能支持，以交付高质量的软件。我们将开发领先的方法和工具，以实现O1.1）提供智能克隆管理基础设施，可以跟踪克隆进化并根据需要更多关注的各种标准（例如，故障倾向）对克隆进行排名； O1.2）提取可重用组件作为应用程序编程接口（API），以重用通常嵌入克隆代码中的功能； O1.3）识别并推荐 API 使用模式以演示如何实现特定功能。 O2) 通过识别最具影响力的缺陷来提高软件的感知质量。鉴于软件应用程序的用户基础庞大且广泛，需要新的质量改进流程和方法来确保开发的软件具有高质量。我们将： O2.1）提出感知质量（即用户体验和满意度）的概念，以识别需要首先修复的高影响缺陷； O2.2）优化预发布工作的时间安排，以确保软件及时发布，但在测试期间捕获高影响的缺陷。 拟议的研究重点是开发通过工业环境中的实证研究进行测试的解决方案。由于我的研究的实际影响，私营公司（例如 IBM）和开源社区（例如 Mozilla 基金会）对采用我们的研究成果表现出了浓厚的兴趣。拟议研究的结果将帮助从业者以有效的方式交付可靠且理想的软件。将感知质量融入演进过程有助于从业者专注于能够快速提高感知质量的关键问题，从而提高客户满意度、品牌声誉并最终提高公司收入。拟议的研究将培养 3 名博士生和 2 名硕士生。