Predicting defects using network analysis on dependency graphs

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Predicting defects using network analysis on dependency graphs

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International Conference on Software Engineering archive
Proceedings of the 30th international conference on Software engineering table of contents

Leipzig, Germany

SESSION: Software engineering economics table of contents

Pages 531-540

Year of Publication: 2008

ISBN:978-1-60558-079-1

Authors

Thomas Zimmermann	University of Calgary, Calgary, AB, Canada
Nachiappan Nagappan	Microsoft Research, Redmond, WA, USA

Sponsors

ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

ACM New York, NY, USA

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ABSTRACT

In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on their experience backed by code complexity metrics. But often dependencies exist between various pieces of code over which managers may have little knowledge. These dependencies can be construed as a low level graph of the entire system. In this paper, we propose to use network analysis on these dependency graphs. This allows managers to identify central program units that are more likely to face defects. In our evaluation on Windows Server 2003, we found that the recall for models built from network measures is by 10% points higher than for models built from complexity metrics. In addition, network measures could identify 60% of the binaries that the Windows developers considered as critical-twice as many as identified by complexity metrics.

REFERENCES

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