Intelligent Code Quality Management for Software Evolution

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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.