On Analyzing the Topology of Commit Histories in Decentralized Version Control Systems

update for BASE on Sep 08 2018 22:43:36International audienceEmpirical analysis of software repositories usually deals with linear histories derived from centralized versioning systems. Decentralized version control systems allow a much richer structure of commit histories, which presents features that are typical of complex graph models. In this paper we bring some evidences of how the very structure of these commit histories carries relevant information about the distributed development process. By means of a novel data structure that we formally define, we analyze the topological characteristics of commit graphs of a sample of git projects. Our findings point out the existence of common recurrent structural patterns which identically occur in different projects and can be consider building blocks of distributed collaborative development

The Grizzly, February 28, 2008

Tragedy Strikes at Northern Illinois University • Safety at Ursinus College in Light of the NIU Massacre • Anti-HIV Gel Fails Clinical Trial, Opens Doors • Investigating the Seven-Day Itch • Great Wall vs. China Jade: Local Chinese Dining • Third Annual CoSA Celebration Hits Ursinus in April • Review of SPINTfest at UC • Opinions: NME Scandal: An Outsider\u27s Take; Why Kosovo\u27s Independence Matters • Strong Champs for UC Swim • Gymnastics Prepares for Nationalshttps://digitalcommons.ursinus.edu/grizzlynews/1757/thumbnail.jp

Detecting Outlier Patterns with Query-based Artificially Generated Searching Conditions

In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, national security, etc. However, sub-graph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this work, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined on a real-world academic network, using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs, and is robust to the choice of similarity measures

Boost the Careers of Early-Stage Researchers

Boosting the careers of early-stage researchers at leading research-intensive universities of S&T occurs along highly competitive and selective mechanisms. Nurturing talent for careers in science is a primary concern and interest of the institutions (institutional perspective). In chapter two, we present five tools to boost the scientific careers of early-stage researchers within universities, i.e. research-based education, research master programmes, doctoral schools, guidance to postdoctoral researchers and tenure track.\ua0Most early-stage researchers move to careers outside universities (both research and nonresearch careers). That is why we address intersectoral mobility in chapter three and present\ua0dual career paths, business start-up support and permeability programmes as tools to boost\ua0the careers of researchers who will contribute to business and industry, public services, notfor-profit organisations and society at large (societal perspective). We also address recruitment of talent from outside academia (back) into our institutions.Transmission of transversal skills to early-stage researchers is essential to increase their employability and to make them attractive on the labour market (individual perspective). Since\ua0universities cannot predict which early-stage researcher will have what kind of career, both generic scientific skills and skills to increase employability are to be strengthened in parallel as\ua0described in chapter four.In chapter five, we introduce metrics as a well-established and indispensable tool in the recruitment, performance assessment and career development of early-stage researchers. We thereby differentiate between common HR metrics and next-generation metrics. The tricky question, of course, is how to safeguard the career perspectives of early-stage researchers while taking into account the reality of the wide-spread usage of some conventional andcontroversial metrics, such as publication in high impact journals.In chapter six, we address guidance and support measures for early-stage researchers.\ua0Universities need to offer career development tools, equal opportunities and family-friendly\ua0environment and infrastructure and support staff.While the chapters two to six contain our descriptions of the issues and present our findings\ua0on the tools, the final chapter seven contains concrete (hands-on) recommendations to department heads, HR professionals, university leaders and policy-makers and funders

Peregrine: A Pattern-Aware Graph Mining System

Graph mining workloads aim to extract structural properties of a graph by exploring its subgraph structures. General purpose graph mining systems provide a generic runtime to explore subgraph structures of interest with the help of user-defined functions that guide the overall exploration process. However, the state-of-the-art graph mining systems remain largely oblivious to the shape (or pattern) of the subgraphs that they mine. This causes them to: (a) explore unnecessary subgraphs; (b) perform expensive computations on the explored subgraphs; and, (c) hold intermediate partial subgraphs in memory; all of which affect their overall performance. Furthermore, their programming models are often tied to their underlying exploration strategies, which makes it difficult for domain users to express complex mining tasks. In this paper, we develop Peregrine, a pattern-aware graph mining system that directly explores the subgraphs of interest while avoiding exploration of unnecessary subgraphs, and simultaneously bypassing expensive computations throughout the mining process. We design a pattern-based programming model that treats "graph patterns" as first class constructs and enables Peregrine to extract the semantics of patterns, which it uses to guide its exploration. Our evaluation shows that Peregrine outperforms state-of-the-art distributed and single machine graph mining systems, and scales to complex mining tasks on larger graphs, while retaining simplicity and expressivity with its "pattern-first" programming approach.Comment: This is the full version of the paper appearing in the European Conference on Computer Systems (EuroSys), 202