Intelligent Code Quality Management for Software Evolution

软件应用程序在我们的日常生活中无处不在。我们依靠它们来支付账单、购物和在线观看视频。它们的质量是至关重要的，不能因为它们不断增加的用户基础和编程复杂性而受到损害。为了确保软件应用程序的质量，提供了软件分析来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，以帮助开发人员提前识别缺陷，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有以下局限性：(1)缺乏工业适应性，因为先前的研究不能很好地扩展来处理越来越多的软件运行数据（例如，崩溃报告）；(2)很少考虑最终用户体验到的软件感知质量，并平等对待所有缺陷。在拟议的研究中，我们的目标是在以下目标中解决上述限制。01)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。我们将开发先进的方法和工具，以1.1)提供智能克隆管理基础设施，可以跟踪克隆的进化，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），以重用通常嵌入在克隆代码中的功能；和O1.3)识别和推荐API使用模式，以演示如何实现某个功能。O2)通过识别最具影响的缺陷来提高软件的感知质量。鉴于软件应用程序的庞大而广泛的用户基础，需要新的质量改进过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，以确定首先要修复的高影响缺陷；和O2.2)优化发布前工作的时间，以确保软件以及时的方式发布，但是在测试期间捕获高影响缺陷。拟议的研究重点是通过在工业环境中进行实证研究来测试解决方案的发展。由于我的研究的实际影响，私营公司（如IBM）和开源社区（如Mozilla基金会）对采用我们的研究成果表现出了浓厚的兴趣。建议的研究结果将帮助实践者以有效的方式交付可靠和理想的软件。将感知质量集成到演进过程中可以帮助从业者关注能够快速提高感知质量的关键问题，从而增加客户满意度、品牌声誉和最终的公司收入。拟进行的研究将培养3名博士和2名硕士研究生。

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.