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Detecting Semantic Clones in Microservices Using Components

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Abstract

The growing popularity of enterprise technologies for decentralized systems leads to commonalities in using components. This direction, however, opens new challenges to code clone detection. Approaches can no longer look at the low-level code but must deal with the higher-level component semantics. Yet, not many works addressed this trend. One of the quality issues that can be identified in large systems is duplicated behavior with different syntactic structures. It is crucial to detect these issues for enterprises where software’s codebase(s) grows and evolves, and maintenance costs rise significantly. This issue is referred to as a semantic clone. The detection of semantic clones requires semantic information about the given program. Unfortunately, while many code clone detection techniques are proposed, there is a lack of solutions targeted explicitly toward enterprise systems and even fewer solutions dedicated to semantic clones. To reason about semantic clones, we consider different pairs of component call-graphs in the system. Since different component types are common in enterprise systems, we can ensure that only relevant fragments are matched, using targeted enterprise metadata. When applied to an established system benchmark, our method indicates high accuracy in detecting semantic clones. We also assessed different system versions to elaborate on the method’s applicability to decentralized system evolution.

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Data availability

The dataset generated in this work is available at https://doi.org/10.5281/zenodo.7632839 and https://doi.org/10.5281/zenodo.7632842. Our prototype tools are available at GitHub as open source: Semantic Clone Detector: https://github.com/cloudhubs/Distributed-Systems-Semantic-Clone-Detector  Gradle Plugin:  https://github.com/cloudhubs/prophet-gradle-plugin,  Interactive Tool: https://github.com/svacina/prophet.

Notes

  1. Train-Ticket benchmark: https://github.com/FudanSELab/train-ticket, accessed on 2/5/2023.

  2. Wanxin benchmark: https://github.com/mikuhuyo/wanxin-p2p, accessed on 2/5/2023.

  3. Swarm benchmark: https://github.com/macrozheng/mall-swarm, accessed on 2/5/2023.

  4. Syntactic Clone results from [22]: https://microservicedata.github.io, accessed on 2/5/2023.

  5. Note that while the examples and implementation demonstrations of our method are specific to the Java platform, it is not limited to just this platform

  6. The domain of this binary output is usually modeled in one of two ways: \(\{1,0\}\) or \(\{1,-1\}\) for positive and negative classes respectively. We assume the \(\{1,0\}\) model for the purposes of the explanation.

  7. Our Prototype:https://github.com/cloudhubs/Distributed-Systems-Semantic-Clone-Detector, accessed on 2/5/2023.

  8. WS4J: https://github.com/Sciss/ws4j, accessed on 2/5/2023.

  9. Our Semantic Clone Dataset (V1): https://zenodo.org/record/7632839, accessed on 2/11/2023.

  10. Our Semantic Clone Dataset (V2): https://zenodo.org/record/7632842, accessed on 2/11/2023.

  11. SonarQube: https://www.sonarqube.org, accessed on 2/5/2023.

  12. Our Interactive Tool: https://github.com/svacina/prophet, accessed on 2/5/2023.

  13. VSCode IDE: https://code.visualstudio.com, accessed on 2/5/2023.

  14. Our Plugin: https://github.com/cloudhubs/prophet-gradle-plugin, accessed on 2/5/2023.

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 1854049 and a grant from Red Hat Research.

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  1. Department of Computer Science, Baylor University, One Bear Place #97141, Waco, TX, 76798, USA

    Amr S. Abdelfattah, Alejandro Rodriguez, Andrew Walker & Tomas Cerny

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This article is part of the topical collection “Advances on Cloud Computing and Services Science” guest edited by Donald F. Ferguson, Claus Pahl and Maarten van Steen.

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Abdelfattah, A.S., Rodriguez, A., Walker, A. et al. Detecting Semantic Clones in Microservices Using Components. SN COMPUT. SCI. 4, 470 (2023). https://doi.org/10.1007/s42979-023-01910-1

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