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Detecting software design defects using relational association rule mining

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Abstract

In this paper, we are approaching, from a machine learning perspective, the problem of automatically detecting defective software entities (classes and methods) in existing software systems, a problem of major importance during software maintenance and evolution. In order to improve the internal quality of a software system, identifying faulty entities such as classes, modules, methods is essential for software developers. As defective software entities are hard to identify, machine learning-based classification models are still developed to approach the problem of detecting software design defects. We are proposing a novel method based on relational association rule mining for detecting faulty entities in existing software systems. Relational association rules are a particular type of association rules and describe numerical orderings between attributes that commonly occur over a dataset. Our method is based on the discovery of relational association rules for identifying design defects in software. Experiments on open source software are conducted in order to detect defective classes in object-oriented software systems, and a comparison of our approach with similar existing approaches is provided. The obtained results show that our method is effective for software design defect detection and confirms the potential of our proposal.

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Acknowledgments

The authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions to improve the paper and the presentation.

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Authors and Affiliations

  1. Department of Computer Science, Babeş-Bolyai University, Cluj–Napoca, Romania

    Gabriela Czibula, Zsuzsanna Marian & Istvan Gergely Czibula

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  1. Gabriela Czibula
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  2. Zsuzsanna Marian
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  3. Istvan Gergely Czibula
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Correspondence to Istvan Gergely Czibula.

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Czibula, G., Marian, Z. & Czibula, I.G. Detecting software design defects using relational association rule mining. Knowl Inf Syst 42, 545–577 (2015). https://doi.org/10.1007/s10115-013-0721-z

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  • DOI: https://doi.org/10.1007/s10115-013-0721-z

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