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Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code

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Knowledge Science, Engineering and Management (KSEM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11775))

Abstract

Software defect prediction has extensive applicability thus being a very active research area in Search-Based Software Engineering. A high proportion of the software defects are caused by violated couplings. In this paper, we investigate the relevance of semantic coupling in assessing the software proneness to defects. We propose a hybrid classification model combining Gradual Relational Association Rules with Artificial Neural Networks, which detects the defective software entities based on semantic features automatically learned from the source code. The experiments we have performed led to results that confirm the interplay between conceptual coupling and software defects proneness.

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Authors and Affiliations

  1. Department of Computer Science, Babeş-Bolyai University, 1, M. Kogalniceanu Street, 400084, Cluj-Napoca, Romania

    Diana-Lucia Miholca & Gabriela Czibula

Authors
  1. Diana-Lucia Miholca
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  2. Gabriela Czibula
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Corresponding author

Correspondence to Diana-Lucia Miholca .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Christos Douligeris

  2. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  3. University of Piraeus, Piraeus, Greece

    Dimitris Apostolou

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Miholca, DL., Czibula, G. (2019). Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_23

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  • DOI: https://doi.org/10.1007/978-3-030-29551-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29550-9

  • Online ISBN: 978-3-030-29551-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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