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Title: Mining developer communication streams
Keywords: Data mining;Data stream mining;Hoeffding Tree;Adaptive sliding window;Jazz
Publisher: Academy & Industry Research Collaboration Center (AIRCC) Publishing Corporation
Description: This paper explores the concepts of modelling a software development project as a process that results in the creation of a continuous stream of data. In terms of the Jazz repository used in this research, one aspect of that stream of data would be developer communication. Such data can be used to create an evolving social network characterized by a range of metrics. This paper presents the application of data stream mining techniques to identify the most useful metrics for predicting build outcomes. Results are presented from applying the Hoeffding Tree classification method used in conjunction with the Adaptive Sliding Window (ADWIN) method for detecting concept drift. The results indicate that only a small number of the available metrics considered have any significance for predicting the outcome of a build.
URI: http://localhost/handle/10292/7077
Other Identifiers: Fourth International Conference on Computer Science & Information Technology held at Pullman Hotel, Sydney, 2014-02-21 to 2014-02-22, published in: CS & IT-CSCP 2014, pp.13 - 25
978-1-921987-27-4
2231-5403
http://hdl.handle.net/10292/7077
Type Of Material: Conference Contribution
Appears in Collections:SERL - Software Engineering Research Laboratory

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