Intelligent Analysis for Crowdsourced Software Engineering ProblemsOrganizers:
Bin Li, Yangzhou University, China. Email: email@example.com
Xiaobing Sun, Yangzhou University, China. Email: firstname.lastname@example.org
Shaowei Wang, Queens University, Canada. Email: email@example.comIntroduction:
Crowdsourcing is an emerging distributed problem-solving model based on the combination of human and machine computation. It is increasingly revolutionizing the ways in which software is engineered, which is called crowdsourced software engineering (as defined by Ke Mao, Licia Capra, Mark Harman and Yue Jia). It has been implemented by many successful crowdsourcing platforms, such as StackOverflow, TopCoder, Google’s Android market, Testin and Bugcrowd. Crowdsourced software engineering utilizes an open call format to recruit global online software engineers, to work on various types of software engineering tasks, such as requirements extraction, design, coding, debugging and testing. This emerging model has been widely studied and practiced in both academic and industrial world. More and more methods and techniques are being developed to solve crowdsourced software engineering problems. Among them, intelligence analysis techniques, such as data mining, information retrieval, machine learning, and statistical analysis, have become a mainstream research area in this field. This special section on intelligent analysis for crowdsourced software engineering Problems will discuss experience reports, tools and techniques that use intelligent analysis to solve crowdsourced software engineering problems, or demonstrate how intelligent analysis can enrich crowdsourced software engineering practice.
Knowledge Representation, Reasoning, and Online Learning in Big Data AnalysisOrganizers:
Ming Yang, Nanjing Normal University, China. Email: firstname.lastname@example.org
Yang Gao, Nanjing University, China. Email: email@example.com
Wensheng Zhang, Institute of Automation of Chinese Academy of Sciences, China. Email: firstname.lastname@example.org
Wanqi Yang, Nanjing Normal University, China. Email: email@example.comIntroduction:
As the data volume extremely increases, big data has become a hot spot. To obtain the valuable information from big data, big data analysis is becoming more and more significant to exploit the inherent rules in original data. Due to the challenging characteristics of big data, i.e., volume, velocity, variety, veracity and value, these traditional analysis techniques (designed for small data) are difficult or even infeasible to deal with big data. For example, the complexity of big data aggravates the difficulty of knowledge representation. Recently, new learning theories and techniques of big data analysis have attracted much attention in both academic and industrial communities. Several existing methods have been successfully presented in many applications, e.g., social network, e-business, information retrieve, multimedia analysis, etc. The special session on knowledge representation, reasoning and online learning in big data analysis will focus on the latest developments about learning theories and methods of big data analysis. Specifically, it is welcome to discuss about several novel methods to address (1) efficient knowledge representation for complex structure, (2) induction/game/reasoning for complex decision-making and (3) fast online statistical learning.