Managing Trace Data Volume through a Heuristical Clustering Process Based on Event Execution Frequency

Authors:
Andy Zaidman;Serge Demeyer
Affiliations:
-;-
Venue:
CSMR '04 Proceedings of the Eighth Euromicro Working Conference on Software Maintenance and Reengineering (CSMR'04)
Year:
2004

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Abstract

To regain architectural insight into a program using dynamicanalysis, one of the major stumbling blocks remainsthe large amount of trace data collected. Therefore, this paperproposes a heuristic which divides the trace data intorecurring event clusters. To compose such clusters the Euclidiandistance is used as a dissimilarity measure on thefrequencies of the events. Manual inspection of these eventsequences revealed that the heuristic provides interestingstarting points for further examination.