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Bmco-o: a smart code smell detection method based on co-occurrences

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Abstract

Code smell detection is a task aimed at identifying sub-optimal programming structures within code entities that may indicate problems requiring attention. It plays a crucial role in improving software quality. Numerous automatic or semi-automatic methods for code smell detection have been proposed. However, these methods are constrained by the manual setting of detection rules and thresholds, leading to subjective determinations, or they require large-scale labeled datasets for model training. In addition, they exhibit poor detection performance across different projects. Related studies have revealed the existence of co-occurrences among different types of code smells. Therefore, we propose a smart code smell detection method based on code smell co-occurrences, termed BMCo-O. The key insight is that code smell co-occurrences can assist in improving code smell detection. We introduce and utilize code smell co-occurrence impact factor set, a code smell pre-filter mechanism, and a possibility mechanism, which enable BMCo-O to demonstrate outstanding detection performance. To reduce manual intervention, we propose an adaptive detection mechanism that automatically adjusts parameters to detect different types of code smell in various software projects. As an initial attempt, we applied the proposed method to seven classical high-criticality code smells: Message Chain, Feature Envy, Spaghetti Code, Large Class, Complex Class, Refused Bequest, and Long Method. The evaluation results on benchmarks composed of open source software projects demonstrated that BMCo-O significantly outperforms the well-known and widely used methods in detecting these seven classical code smells, especially in F1, with improvements of 137%, 155%, 23%, 195%, 364%, 552% and 35%, respectively. To further verify its effectiveness in actual detection across different software projects, we also implemented a prototype of a new code smell detector using BMCo-O.

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Data availability

https://github.com/SilverMustang/BMCo-O

Notes

  1. CppDepend. 2021. https://www.cppdepend.com/

  2. Designite. 2021. https://www.designite-tools.com/

  3. NDepend. 2021. https://www.ndepend.com/

  4. Sonarsource. 2021. https://www.sonarsource.com/

  5. PMD. 2021. https://pmd.github.io/pmd-6.29.0/index.html

  6. SDMetrics. 2021. https://www.sdmetrics.com/index.html

  7. https://github.com/SilverMustang/BMCo-O

  8. optimized Palambo dataset.zip, https://github.com/SilverMustang/BMCo-O

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Acknowledgements

The authors would like to thank anonymous reviewers for their insightful and constructive comments. This work was supported by the National Nature Science Foundation of China (Number 62176164), the Natural Science Foundation of Guangdong Province (Grant 2023A1515010992), Shenzhen Science and Technology Foundation (JCYJ20220531101217039 and JCYJ20210324093212034).

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  1. College of Computer Science and Software Engineering, Shenzhen University, Nanshan District, Shenzhen, 518060, Guangdong, China

    Feiqiao Mao, Kaihang Zhong & Long Cheng

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  1. Feiqiao Mao
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Contributions

Feiqiao Mao made substantial contributions to the conception and design of the work; revised the draft of the work critically for important intellectual content. Kaihang Zhong made the acquisition, analysis, interpretation of data; and the creation of new software used in the work; drafted the work. Feiqiao Mao and Kaihang Zhong wrote the main manuscript text and Long Cheng prepared tables 1-4, 21-23, figures 2-3 and the latex sources files of the manuscript. All authors reviewed the manuscript. Feiqiao Mao and Long Cheng revised the manuscript according to the review comments.

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Correspondence to Feiqiao Mao.

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Mao, F., Zhong, K. & Cheng, L. Bmco-o: a smart code smell detection method based on co-occurrences. Autom Softw Eng 32, 24 (2025). https://doi.org/10.1007/s10515-025-00486-9

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