I am a NSF funded Ph.D. student in Department of Computer Science at North Carolina State University under supervision of Dr. Tim Menzies. I joined the Real-world Artificial Intelligence for Software Engineering (RAISE) lab in 2017.
Before coming to NC State, I earned my M.S. degree of Computer Science from The University of Texas at Dallas in 2015, where I also finished courseworks of Electical and Computer Engineering (mainly focusing on VLSI Circuit Design and Computer Architecture).I did my undergrad at Nanjing University of Posts and Telecommunications, China, where I got my B.S. degree of Microelectronics in 2013 summer.
My research interests include using data mining and artificial intelligence methods to solve real world problems in software engineering field. Such as exploring new techniques in search-based optimization to improve the performance of current SE predicting tasks (like effort estimation, text mining, etc.). I believe these software engineering tasks are not always need to be hard (but it may not be easy to find the easy ways to do it), and enjoy finding path to make them better and better.
Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g. defect prediction and text mining) but, so far, has not been extensively explored for effort estimation. Accordingly, this paper seeks simple, automatic, effective and fast methods for finding good tunings for automatic software effort estimation.