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Software-Bug Localization with Graph Mining

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Managing and Mining Graph Data

Part of the book series: Advances in Database Systems ((ADBS,volume 40))

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.

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Authors and Affiliations

  1. Institute for Program Structures and Data Organization (IPD), Universita̶t Karlsruhe (TH), Karlsruhe, Germany

    Frank Eichinger & Klemens Bo̶hm

Authors
  1. Frank Eichinger
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  2. Klemens Bo̶hm
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Correspondence to Frank Eichinger .

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Editors and Affiliations

  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, U.S.A.

    Charu C. Aggarwal

  2. Microsoft Research Asia, Zhichun Road 49, Beijing, 100080, China, People's Republic

    Haixun Wang

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Eichinger, F., Bo̶hm, K. (2010). Software-Bug Localization with Graph Mining. In: Aggarwal, C., Wang, H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_17

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  • DOI: https://doi.org/10.1007/978-1-4419-6045-0_17

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  • Print ISBN: 978-1-4419-6044-3

  • Online ISBN: 978-1-4419-6045-0

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