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  1. Proceedings of the 1st International Workshop on Crowd-based Software Development Methods and Technologies (CrowdSoft 2014)
  2. Recommending relevant projects via user behaviour: an exploratory study on github
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How the crowd impacts commercial applications: a user-oriented approach
Recommending relevant projects via user behaviour: an exploratory study on github
SmartHR: a resume query and management system based on semantic web
Crowdsourcing in the Brazilian IT industry: what we know and what we don't know
Exploring the patterns of social behavior in GitHub
Personalized mobile application discovery
Using clustering and transitivity to reduce the costs of crowdsourced entity resolution
Investigating social media in GitHub's pull-requests: a case study on Ruby on Rails
A novel multilayered context awareness technology for internetware evolution
iTest: testing software with mobile crowdsourcing
Estimating the dynamic performance of composed services: a probability theory based approach

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Recommending relevant projects via user behaviour: an exploratory study on github

Content Provider ACM Digital Library
Author Zhu, Zixiao Xie, Bing Zou, Yanzhen Zhang, Lingxiao
Abstract Social coding sites (e.g., Github) provide various features like Forking and Sending Pull-requests to support crowd-based software engineering. When using these features, a large amount of user behavior data is recorded. User behavior data can reflect developers preferences and interests in software development activities. Online service providers in many fields have been using user behavior data to discover user preferences and interests to achieve various purposes. In the field of software engineering however, there has been few studies in mining large amount of user behavior data. Our goal is to design an approach based on user behavior data, to recommend relevant open source projects to developers, which can be helpful in activities like searching for the right open source solutions to quickly build prototypes. In this paper, we explore the possibilities of such a method by conducting a set of experiments on selected data sets from Github. We find it a promising direction in mining projects' relevance from user behavior data. Our study also obtain some important issues that is worth considering in this method. .
Starting Page 25
Ending Page 30
Page Count 6
File Format PDF
ISBN 9781450332248
DOI 10.1145/2666539.2666570
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-17
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Crowd-base software engineering Github Social coding Recommendation system
Content Type Text
Resource Type Article
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