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International Conference on Evaluation of Novel Approaches to Software Engineering

ENASE 2018: Evaluation of Novel Approaches to Software Engineering pp 137–164Cite as

Detecting Behavioral Design Patterns from Software Execution Data

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1023))

Abstract

Design pattern detection techniques provide useful insights to help understand the design and architecture of software systems. Existing design pattern detection techniques require as input the source code of software systems. Hence, these techniques may become not applicable in case the source code is not available anymore. Large volumes of data are recorded and stored during software execution, which is very useful for design pattern detection of software systems. This chapter introduces a general framework to support the detection of behavioral design patterns by taking as input the software execution data. To show the effectiveness, the proposed framework is instantiated for the observer, state and strategy patterns. The developed pattern detection techniques are implemented in the open-source process mining toolkit ProM. The applicability of the proposed framework is evaluated using software execution data containing around 1.000.000 method calls that are generated by running both synthetic and real-life software systems.

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Notes

  1. 1.

    http://www.promtools.org/.

  2. 2.

    https://svn.win.tue.nl/repos/prom/Packages/SoftwareProcessMining/.

  3. 3.

    http://essere.disco.unimib.it/svn/DPB/Lexi%20v0.1.1%20alpha/.

  4. 4.

    http://www.inf.fu-berlin.de/lehre/WS99/java/swing/JHotDraw5.1/.

  5. 5.

    http://essere.disco.unimib.it/svn/DPB/JUnit%20v3.7/.

  6. 6.

    http://essere.disco.unimib.it/svn/DPB/MapperXML%20v1.9.7/.

  7. 7.

    http://www.3tu-bsr.nl/doku.php?id=start.

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Acknowledgements

This work is supported by the NIRICT 3TU.BSR (Big Software on the Run) research projectFootnote 7

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Authors and Affiliations

  1. Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands

    Cong Liu, Boudewijn F. van Dongen, Nour Assy & Wil M. P. van der Aalst

  2. RWTH Aachen University, 52056, Aachen, Germany

    Wil M. P. van der Aalst

Authors
  1. Cong Liu
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  2. Boudewijn F. van Dongen
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  3. Nour Assy
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  4. Wil M. P. van der Aalst
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Corresponding author

Correspondence to Cong Liu .

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Editors and Affiliations

  1. Khalifa University, Abu Dhabi, United Arab Emirates

    Ernesto Damiani

  2. City University London, London, UK

    George Spanoudakis

  3. Wrocław University of Economics, Wroclaw, Poland

    Leszek A. Maciaszek

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Liu, C., van Dongen, B.F., Assy, N., van der Aalst, W.M.P. (2019). Detecting Behavioral Design Patterns from Software Execution Data. In: Damiani, E., Spanoudakis, G., Maciaszek, L. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2018. Communications in Computer and Information Science, vol 1023. Springer, Cham. https://doi.org/10.1007/978-3-030-22559-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-22559-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22558-2

  • Online ISBN: 978-3-030-22559-9

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