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Software-Bug Localization with Graph Mining

Eichinger, Frank; Böhm, Klemens

Abstract:
In the recent past, a number of frequent subgraph mining algorithms has been proposed They allow for analyses in domains where data is naturally graphstructured. However, caused by scalability problems when dealing with large graphs, the application of graph mining has been limited to only a few domains. In software engineering, debugging is an important issue. It is most challenging to localize bugs automatically, as this is expensive to be done manually. Several approaches have been investigated, some of which analyze traces of repeated program executions. These traces can be represented as call graphs. Such graphs describe the invocations of methods during an execution. This chapter is a survey of graph mining approaches for bug localization based on the analysis of dynamic call graphs. In particular, this chapter first introduces the subproblem of reducing the size of call graphs, before the different approaches to localize bugs based on such reduced graphs are discussed. Finally, we compare selected techniques experimentally and provide an outlook on future issues.



Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Buchaufsatz
Publikationsjahr 2010
Sprache Englisch
Identifikator ISBN: 978-1-4419-6044-3
ISSN: 1386-2944
KITopen-ID: 1000015591
Erschienen in Managing and Mining Graph Data. Ed.: C. C. Aggarwal
Verlag Springer US, Berlin
Seiten 515 - 546
Serie Advances in Database Systems ; 40
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