Title :
Profile Mining in CVS-Logs and Face-to-Face Contacts for Recommending Software Developers
Author :
Macek, Bjoern-Elmar ; Atzmueller, Martin ; Stumme, Gerd
Author_Institution :
Knowledge & Data Eng. Group (KDE), Univ. of Kassel, Kassel, Germany
Abstract :
In order to support a software development team in its day-to-day operations, different data sources can be exploited. In this paper, we focus on CVS logs and communication profiles between developers provided by RFID-proximity information. We provide a novel approach for combining the data sources into a graph, and apply the page rank algorithm for capturing interesting knowledge about resource and developer profiles. Additionally, we discuss the application in the software developer setting, and also for project management. The proposed approach is evaluated in the context of a real-world developer setting.
Keywords :
concurrency control; configuration management; data mining; graph theory; project management; radiofrequency identification; software development management; RFID-proximity information; communication profiles; concurrent version system-logs; face-to-face contacts; graph; page rank algorithm; profile mining; project management; software developer recommendation; software development team; Data mining; Knowledge transfer; Probability distribution; Programming; Radiofrequency identification; Software; Vectors; PageRank; Reality Mining; Recommender; Social Web;
Conference_Titel :
Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4577-1931-8
DOI :
10.1109/PASSAT/SocialCom.2011.40
