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URN: urn:nbn:de:swb:90-230497

Scalable Software-Defect Localisation by Hierarchical Mining of Dynamic Call Graphs

Eichinger, Frank; Oßner, Christopher; Böhm, Klemens

The localisation of defects in computer programmes is essential in software engineering and is important in domain-specific data mining. Existing techniques which build on call-graph mining localise defects well, but do not scale for large software projects. This paper presents a hierarchical approach with good scalability characteristics. It makes use of novel call-graph representations, frequent subgraph mining and feature selection. It first analyses call graphs of a coarse granularity, before it zooms-in into more fine-grained graphs. We evaluate our approach with defects in the Mozilla Rhino project: In our setup, it narrows down the code a developer has to examine to about 6% only.

Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Jahr 2011
Sprache Englisch
Identifikator ISBN: 978-0-898719-92-5
KITopen ID: 1000023049
Erschienen in Proceedings of the 11th SIAM International Conference on Data Mining (SDM'11), Meza, Ariz., USA, April 28-30 2011
Verlag Omnipress, S.l.
Schlagworte applied data mining, call graphs, graph mining, scalability, software-defect localisation
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