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CoRT: Transformer-based code representations with self-supervision by predicting reserved words for code smell detection

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Abstract

Context

Code smell detection is the process of identifying poorly designed and implemented code pieces. Machine learning-based approaches require enormous amounts of manually labeled data, which are costly and difficult to scale. Unsupervised semantic feature learning, or learning without manual annotation, is vital for effectively harvesting an enormous amount of available data.

Objective

The objective of this study is to propose a new code smell detection approach that utilizes self-supervised learning to learn intermediate representations without the need for labels and then fine-tune these representations on multiple tasks.

Method

We propose a Code Representation with Transformers (CoRT) to learn the semantic and structural features of the source code by training transformers to recognize masked reserved words that are applied to the code given as input. We empirically demonstrated that the defined proxy task provides a powerful method for learning semantic and structural features. We exhaustively evaluated our approach on four downstream tasks: detection of the Data Class, God Class, Feature Envy, and Long Method code smells. Moreover, we compare our results with those of two paradigms: supervised learning and a feature-based approach. Finally, we conducted a cross-project experiment to evaluate the generalizability of our method to unseen labeled data.

Results

The results indicate that the proposed method has a high detection performance for code smells. For instance, the detection performance of CoRT on Data Class achieved a score of F1 between 88.08–99.4, Area Under Curve (AUC) between 89.62–99.88, and Matthews Correlation Coefficient (MCC) between 75.28–98.8, while God Class achieved a value of F1 ranges from 86.32–99.03, AUC of 92.1–99.85, and MCC of 76.15–98.09. Compared with the baseline model and feature-based approach, CoRT achieved better detection performance and had a high capability to detect code smells in unseen datasets.

Conclusions

The proposed method has been shown to be effective in detecting class-level, and method-level code smells.

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Data Availability

The datasets generated and/or analyzed in the current study are available upon acceptance at the GitHub repository: https://github.com/amalazba/CoRT-Transformer-based-Code-Representations-with-Self-supervision-for-Code-Smell-Detection.

Notes

  1. https://scale.com/pricing

  2. https://github.com/amalazba/CoRT-Transformer-based-Code-Representations-with-Self-supervision-for-Code-Smell-Detection

  3. https://zenodo.org/record/3992730#.YvsvfuxBxEI

  4. https://zenodo.org/record/6555241#.YvssBuxByi4

  5. https://zenodo.org/record/4103861#.Yvsyu-xByi4

  6. https://www.tensorflow.org/

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Acknowledgements

The authors acknowledge the support of the King Fahd University of Petroleum and Minerals in the development of this work.

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Authors and Affiliations

  1. Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Amal Alazba, Hamoud Aljamaan & Mohammad Alshayeb

  2. Department of Information Systems, King Saud University, 11362, Riyadh, Saudi Arabia

    Amal Alazba

  3. Interdisciplinary Research Center for Finance and Digital Economy, 31261, Dhahran, Saudi Arabia

    Hamoud Aljamaan

  4. Interdisciplinary Research Center for Intelligent Secure Systems, 31261, Dhahran, Saudi Arabia

    Mohammad Alshayeb

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  1. Amal Alazba
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Correspondence to Amal Alazba.

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Appendix 1: Cross-project Performance Heatmap (Accuracy, Precision, Recall)

Appendix 1: Cross-project Performance Heatmap (Accuracy, Precision, Recall)

See Figs. 12, 13, 14 and 15.

Fig. 12
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Cross-project performance heatmap for Data Class

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Fig. 13
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Cross-project performance heatmap for God Class

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Fig. 14
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Cross-project performance heatmap for Feature Envy

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Fig. 15
figure 15

Cross-project performance heatmap for Long Method

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Alazba, A., Aljamaan, H. & Alshayeb, M. CoRT: Transformer-based code representations with self-supervision by predicting reserved words for code smell detection. Empir Software Eng 29, 59 (2024). https://doi.org/10.1007/s10664-024-10445-9

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