Discovering dynamic developer relationships from software version histories by time series segmentation

Harvey Siy, Parvathi Chundi, Daniel J. Rosenkrantz, Mahadevan Subramaniam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Time series analysis is a pmmising appmach to discover temporal patterns from time stamped, numeric data. A novel approach to apply time series analysis to discern temporal information from software version repositories is proposed. Version logs containing numeric as well as non-numeric data are represented as an item-set time series. A dynamic programming based algorithm to optimally segment an item-set time series is presented. The algorithm automatically produces a compacted item-set time series that can be analyzed to discern temporal patterns. The effectiveness of the approach is illustrated by applying to the Mozilla data set to study the change frequency and developer activity profiles. The experimental results show that the segmentation algorithm produces segments that capture meaningful information and is superior to the information content obtaining by arbitrarily segmenting time period into regular time intervals.

Original languageEnglish (US)
Title of host publicationICSM 2007 - Proceedings of the 2007 IEEE International Conference on Software Maintenance
Pages415-424
Number of pages10
DOIs
StatePublished - 2007
Event23rd International Conference on Software Maintenance, ICSM - Paris, France
Duration: Oct 2 2007Oct 5 2007

Publication series

NameIEEE International Conference on Software Maintenance, ICSM

Conference

Conference23rd International Conference on Software Maintenance, ICSM
CountryFrance
CityParis
Period10/2/0710/5/07

ASJC Scopus subject areas

  • Software

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