报告题目1：Mining Software Repositories to Handle the Complexity of Software Development
报告人： 夏鑫 博士
报告人单位： 浙江大学 / 蒙纳士大学（澳洲）
Xin Xia will join the Faculty of Information Technology, Monash University, Australia as a lecturer (equivalent to U.S. assistant professor) from January, 2018. Prior to joining Monash University, he was a post-doctoral research fellow in the software practices lab at the University of British Columbia in Canada, and a research assistant professor at Zhejiang University in China. Xin received both of his Ph.D and bachelor degrees in computer science and software engineering from Zhejiang University in 2014 and 2009, respectively. Xin has published 88 papers to date, and many papers appeared in premier software engineering venues, such as TSE, EMSE, ASE, and ICSME. To help developers and testers improve their productivity, his current research focuses on mining and analyzing rich data in software repositories to uncover interesting and actionable information. He has employed and customized structured and unstructured data analytics techniques – including data mining, information retrieval, natural language processing, search-based algorithms, and program analysis – to transform passive software engineering data into automated tools and new insights. More details can be found in https://xin-xia.github.io/.
Today, data miners often apply or extend data mining techniques to solve problems across many domains (e.g., social media, health informatics, and software systems); while domain experts leverage their own domain knowledge to solve their own problems. Data miners often apply their automated techniques to solve a wide range of problems across different domains with limited knowledge of the domain; while domain experts often have limited knowledge of automated techniques when solving their domain-specific problems.
My research tries to bridge the gap between both types of experts (i.e., Data miners and Domain Experts). In this talk, I will focus on the software engineering domain and I will give an overview of two challenges facing data miner and domain experts as they make use of automated techniques, in particular: (1) strong performance of techniques is not sufficient, instead a deeper understanding of the domain is essential; (2) results should be presented in a domain-centric context. I will present examples from my research to explain what these challenges are, why do they appear, and my efforts to avoid them.
报告题目2：Mining Android Apps: The Static Analysis Fashion
报告人单位： 卢森堡大学 / 蒙纳士大学（澳洲）
Li Li is a Research Associate at the Serval group, SnT, University of Luxembourg and an Honorary Research Associate at the CREST group, University College London. He will join Monash University, Australia as a Lecturer in February 2018. He received his PhD degree in computer science from the University of Luxembourg. His research interests are in the fields of Android security and Reliability, Static Code Analysis, Empirical Study and Machine Learning. He has published over 20 research papers at top-ranked conferences and journals such as ICSE, POPL, ISSTA, TIFS, IST. He is an active member of the software engineering and security community serving as reviewers for many top-tier journals such as TSE, TIFS, ESE.
Research on Android has boomed in recent years and static code analysis has been used extensively for analysing large-scale Android apps. In this talk, I will introduce the challenges faced by static code analysis approaches that are summarised through a systematic literature review (SLR) and our solutions for addressing some of those highlighted challenges. More specifically, among several challenges addressed in our research, I will particularly present our solutions for tackling the inter-component communication (ICC), Reflection and common library challenges.