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Mining Edge-Weighted Call Graphs to Localise Software Bugs

Eichinger, Frank 1; Böhm, Klemens 1; Huber, Matthias 1
1 Institut für Programmstrukturen und Datenorganisation (IPD), Karlsruher Institut für Technologie (KIT)

Abstract:

An important problem in software engineering is the automated discovery of noncrashing occasional bugs. In this work we address this problem and show that mining of weighted call graphs of program executions is a promising technique. We mine weighted graphs with a combination of structural and numerical techniques. More specifically, we propose a novel reduction technique for call graphs which introduces edge weights. Then we present an analysis technique for such weighted call graphs based on graph mining and on traditional feature selection schemes. The technique generalises previous graph mining approaches as it allows for an analysis of weights. Our evaluation shows that our approach finds bugs which previous approaches cannot detect so far. Our technique also doubles the precision of finding bugs which existing techniques can already localise in principle.


Volltext §
DOI: 10.5445/IR/1000009256
Originalveröffentlichung
DOI: 10.1007/978-3-540-87479-9_40
Scopus
Zitationen: 40
Dimensions
Zitationen: 36
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2008
Sprache Englisch
Identifikator ISBN: 978-3-540-87478-2
ISSN: 0302-9743
urn:nbn:de:swb:90-92566
KITopen-ID: 1000009256
Erschienen in Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008 : European Conference, Antwerp, Belgium, September 15-19, 2008, Proceedings. Bearb.: W. Daelemans
Verlag Springer Verlag
Seiten 333-348
Serie Lecture Notes in Computer Science ; 5211
Bemerkung zur Veröffentlichung Erstveröffentlichung der Publikation im Springer-Verlag in Berlin.
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