Dr. Xiaoyin Wang, assistant professor in the UTSA Department of Computer Science, has been awarded a National Science Foundation (NSF) Faculty Early Career Development (CAREER) Award. The award includes a $492,358 grant to support his research in software development over the next five years. He is the eighth faculty member in Computer Science to receive this award.
His project, CAREER: Analysis and Repair of Build Scripts for DevOps Software Practice, focuses on enhancing software productivity and quality via more robust software build systems. In particular, Wang uses program analyses to detect and repair bugs in the build scripts and configuration files which are executed by developers in their periodical software integration process.
By reducing the bugs in software build systems, Wang’s research will lead to more timely software release and software products of higher quality, which will finally benefit people who directly use those software and who are involved in activities supported by those software.
One major goal of this project is to develop an analysis infrastructure, which will support various static and concolic analyses of build scripts, including the handling of environment dependencies and build script interactions. The infrastructure will enable more in-depth understanding of software build process and various bug detection / repair techniques in the future. If successful, the project will lead to higher-quality build scripts, more productive build script maintenance, fewer delays in software integration process, as well as more robust software products, especially for those supporting different system environments and configurations. The project will also develop set-up tutorials for popular software frameworks based on analysis of their build scripts to help novice software developers learning the frameworks.
Besides this project, Wang is leading few other projects. One project is exploring a more effective method of combinatorial testing (CT). CT seeks to detect potential faults caused by various interactions of factors that can influence the software systems. Wang proposes a novel CT framework that allows both generation and identification process to interact with each other. As a result, both generation and identification stages will be done more effectively and efficiently. In this project, he also developed software performance models to reduce the cost of regression performance testing. Another project is on the privacy analysis and protection for mobile devices, in which he co-developed PoliDroid and GUILeak to detect privacy leaks from Android apps. Other projects look into issues ranging from analysis of software repositories and analyses for biological and digital threats.
Besides the NSF CAREER award, Wang was previously awarded an NSF CRII grant of around $175K and an NSF EAGER grant of $144K. He is also a co-PI of a grant from U.S. Department of Homeland Security, which provides $1.5M on analyses and training for defense of biological and digital threats.