git2net - Mining Time-Stamped Co-Editing Networks from Large git Repositories

Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication from the commit history of projects. Most of the studied networks are based on the co-authorship of software artefacts defined at the level of files, modules, or packages. While this approach has led to insights into the social aspects of software development, it neglects detailed information on code changes and code ownership, e.g. which exact lines of code have been authored by which developers, that is contained in the commit log of software projects. Addressing this issue, we introduce git2net, a scalable python software that facilitates the extraction of fine-grained co-editing networks in large git repositories. It uses text mining techniques to analyse the detailed history of textual modifications within files. This information allows us to construct directed, weighted, and time-stamped networks, where a link signifies that one developer has edited a block of source code originally written by another developer. Our tool is applied in case studies of an Open Source and a commercial software project. We argue that it opens up a massive new source of high-resolution data on human collaboration patterns.Comment: MSR 2019, 12 pages, 10 figure

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

A Data Mining Toolbox for Collaborative Writing Processes

Collaborative writing (CW) is an essential skill in academia and industry. Providing support during the process of CW can be useful not only for achieving better quality documents, but also for improving the CW skills of the writers. In order to properly support collaborative writing, it is essential to understand how ideas and concepts are developed during the writing process, which consists of a series of steps of writing activities. These steps can be considered as sequence patterns comprising both time events and the semantics of the changes made during those steps. Two techniques can be combined to examine those patterns: process mining, which focuses on extracting process-related knowledge from event logs recorded by an information system; and semantic analysis, which focuses on extracting knowledge about what the student wrote or edited. This thesis contributes (i) techniques to automatically extract process models of collaborative writing processes and (ii) visualisations to describe aspects of collaborative writing. These two techniques form a data mining toolbox for collaborative writing by using process mining, probabilistic graphical models, and text mining. First, I created a framework, WriteProc, for investigating collaborative writing processes, integrated with the existing cloud computing writing tools in Google Docs. Secondly, I created new heuristic to extract the semantic nature of text edits that occur in the document revisions and automatically identify the corresponding writing activities. Thirdly, based on sequences of writing activities, I propose methods to discover the writing process models and transitional state diagrams using a process mining algorithm, Heuristics Miner, and Hidden Markov Models, respectively. Finally, I designed three types of visualisations and made contributions to their underlying techniques for analysing writing processes. All components of the toolbox are validated against annotated writing activities of real documents and a synthetic dataset. I also illustrate how the automatically discovered process models and visualisations are used in the process analysis with real documents written by groups of graduate students. I discuss how the analyses can be used to gain further insight into how students work and create their collaborative documents