Software Maintainability Index Prediction with Source Code Metrics using Machine Learning

Kökten Ulaş Birant

Software Maintainability Index Prediction with Source Code Metrics using Machine Learning

Abstract

Software Maintainability Prediction (SMP) plays a significant role in software engineering, as it provides developers and project managers with insights into quality, potential maintenance costs, and scalability over time. In this research, a machine learning–based model for SMP is presented using the M5P Model Tree algorithm. The prediction is performed based on source code metrics, including comment density, cyclomatic complexity, Halstead volume, and cohesion. Experiments conducted on a dataset demonstrated that the trained model achieved a Correlation Coefficient (R) of 0.8892, indicating a high predictive capability. Compared to its counterpart methods, it delivered a 9.23% improvement in terms of R, confirming its superior performance.

Keywords

References

[1] G. Yenduri and T. R. Gadekallu, “A review on soft computing approaches for predicting maintainability of software: State‐of‐the‐art, technical challenges, and future directions,” Expert Systems, no. 7, pp. 1-21, 2023. doi: 10.1111/exsy.13250.
[2] R. Dixit, B. Bhushan, “Test Metrics for Maintainability of Software: A Case Study on Searching and Sorting Programs,” I-manager’s Journal on Software Engineering, vol. 14, no. 3, pp. 29–36, 2020. doi: 10.26634/jse.14.3.17226.
[3] S. Elmidaoui, L. Cheikhi, A. Idri, and A. Abran, “Machine learning techniques for software maintainability prediction: Accuracy analysis,” Journal of Computer Science and Technology, vol. 35, no. 5, pp. 1147-1174, 2020. doi: 10.1007/s11390-020-9668-1.
[4] S. Elmidaoui, L. Cheikhi, A. Idri, and A. Abran, “Empirical studies on software product maintainability prediction: A systematic mapping and review,” e-Informatica Software Engineering Journal, vol. 13, no. 1, pp. 141–202, 2019. DOI: 10.5277/e-Inf190105.
[5] R. Malhotra and K. Lata, “A systematic literature review on empirical studies towards prediction of software maintainability,” Soft Computing, vol. 21, pp. 16655-16677, 2020. doi: 10.1007/s00500-020-05005-4.
[6] H. Alsolai and M. Roper, “A systematic literature review of machine learning techniques for software maintainability prediction,” Information and Software Technology, vol. 119, pp. 1-25, 2020. doi: 10.1016/j.infsof.2019.106214.
[7] V. Goel and A. Kaur, “A Novel Technique for Optimization of Artificial Neural Network Using Ensemble of Chimp, Harris Hawks and Manta Ray Foraging Optimization Algorithms for Enhancing Software Maintainability Prediction,” Arabian Journal for Science and Engineering, vol. 50, pp. 1-35, 2025. doi: 10.1007/s13369-025-10031-1.
[8] A. Vescan and D. Barac-Antonescu, “Software maintainability prediction based on change metric using neural network models,” Engineering Applications of Artificial Intelligence, vol. 144, pp. 1-12, 2025. doi: 10.1016/j.engappai.2025.110032

[9] R. Yadav and R. Singh, “Enhancing Software Maintainability Prediction Using Multiple Linear Regression and Predictor Importance,” International Journal of Experimental Research and Review, vol. 36, pp. 135–146, 2023. doi: 10.52756/ijerr.2023.v36.013.
[10] Y. Hu, H. Jiang and Z. Hu “Measuring code maintainability with deep neural networks,” Frontiers in Computer Science, vol. 17, no. 6, pp. 1-15, 2023. doi: 10.1007/s11704-022-2313-0
[11] S. Gupta and A. Chug “A feature selection strategy for improving software maintainability prediction,” Intelligent Data Analysis, vol. 26, pp. 311–344, 2022. doi: 10.3233/IDA-215825
[12] R. Ranjan and A. Mishra “Presaging Software Maintainability Using Collaborative Techniques and Arranged Broad View,” IEEE World Conference on Applied Intelligence and Computing, Sonbhadra, India, 17-19 June 2022, pp. 115-119. doi: 10.1109/AIC55036.2022.9848894
[13] K.P. Sharma, V. Kumar, D.N. Le "Defects Maintainability Prediction of the Software,", In: Khari, M., Mishra, D.B., Acharya, B., Gonzalez Crespo, R. (eds) Optimization of Automated Software Testing Using Meta-Heuristic Techniques. EAI/Springer Innovations in Communication and Computing. Springer, Cham, 2022. doi: 10.1007/978-3-031-07297-0_10
[14] H. Alsolai, M. Roper "The Impact of Ensemble Techniques on Software Maintenance Change Prediction: An Empirical Study," Applied Sciences, vol. 12, no 10, pp. 1-28, 2022. doi: 10.3390/app12105234
[15] R. Balamurugan and M. Ravichandran "Enhanced convolutional neural network based android software maintainability prediction," International Journal of Health Sciences, vol. 6, no S4, pp. 6391–6403, 2022. doi: 10.53730/ijhs.v6nS4.9616
[16] M. Duhan and P. K. Bhatia, “Hybrid Maintainability Prediction using Soft Computing Techniques,” International Journal of Computing, vol. 20, no. 3, pp. 350-356, 2021. doi: 10.47839/ijc.20.3.2280
[17] N. Iqbal, J. Sang, J. Chen and X. Xia, “Measuring Software Maintainability with Naïve Bayes Classifier,” Entropy, vol. 23, no. 2, 2021. doi: 10.3390/e23020136.
[18] S. Gupta and A. Chung “An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, no. 2, pp. 89-109, 2021. DOI: 10.9781/ijimai.2021.10.002.
[19] R. Malhotra and K. Lata “An empirical study on predictability of software maintainability using imbalanced data,” Software Quality Journal, vol. 28, pp. 1581–1614, 2020. doi: /10.1007/s11219-020-09525-y
[20] M. Gradisnik, T. Beranic and S. Karakatic. "Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development" Applied Sciences, vol, 10, no. 13: pp. 1-30, 2020. doi: 10.3390/app10134624
[21] T. Heričko, B. Šumak, “Exploring Maintainability Index Variants for Software Maintainability Measurement in Object-Oriented Systems,” Applied Sciences, vol. 13, no. 5, pp. 1-36, 2023. doi: 10.3390/app13052972.
[22] R. J. Quinlan, “Learning with Continuous Classes,” 5th Australian Joint Conference on Artificial Intelligence, Hobart, Tasmania, pp. 343–348, 16–18 November 1992.
[23] Y. Wang and I. H. Witten, “Induction of model trees for predicting continuous classes,” 9th European Conference on Machine Learning, Prague, Czech Republic, 23-25 April 1997.
[24] G. Ghanbarzadeh, K. Ghorbani, M. Salarijazi, C.B. Komaki, and L. R. Ghaleh, L. Use of stepwise M5 model tree to forecast the P24max based on teleconnection indices. Atmospheric Science Letters, vol. 26, no. 1, pp. 1-13, 2025. doi: 10.1002/asl.1276
[25] A. Kumar, P. Sihag, K. Singh, F. Esmaeilbeiki, B. Singh, H. Om "Prediction of Air Quality Index (AQI) using machine learning-based tree models—a case study of Gurugram City, India," Theoretical and Applied Climatology, no. 156, pp. 1-18, 2025. doi: 10.1007/s00704-025-05535-7
[26] S. Prakash, B. Kalaiselvi, and M. Batumalay, M. IoT-Enabled Supervised Learning-Based Prediction Model for Smart Instrumentation Controllers in Signal Conditioning Systems. Journal of Applied Data Sciences, vol. 6, no. 4, pp. 2959-2973, 2025. doi: 10.47738/jads.v6i4.803
[27] E. Frank, M. A. Hall, and I. H. Witten, Data Mining: Practical Machine Learning Tools and Techniques, 4th ed., San Francisco, CA, USA: Morgan Kaufmann, 2016.
[28] Kaggle Datasets, “Maintainability Index Code Metrics,” 2025. [Online]. Available: https://www.kaggle.com/datasets/v4ndi101/maintainability-index-code-metrics. [Accessed: Oct. 12, 2025]

Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Kökten Ulaş Birant ^*
0000-0002-5107-6406
Türkiye

Publication Date

May 3, 2026

Submission Date

March 18, 2026

Acceptance Date

April 16, 2026

Published in Issue

Year 2026 Volume: 6 Number: 1

IZ

https://izlik.org/JA27FG86YP

Cite

RIS / Bibtex

APA

Birant, K. U. (2026). Software Maintainability Index Prediction with Source Code Metrics using Machine Learning. Artificial Intelligence Theory and Applications, 6(1), 44-55. https://izlik.org/JA27FG86YP

AMA

1.Birant KU. Software Maintainability Index Prediction with Source Code Metrics using Machine Learning. AITA. 2026;6(1):44-55. https://izlik.org/JA27FG86YP

Chicago

Birant, Kökten Ulaş. 2026. “Software Maintainability Index Prediction With Source Code Metrics Using Machine Learning”. Artificial Intelligence Theory and Applications 6 (1): 44-55. https://izlik.org/JA27FG86YP.

EndNote

Birant KU (May 1, 2026) Software Maintainability Index Prediction with Source Code Metrics using Machine Learning. Artificial Intelligence Theory and Applications 6 1 44–55.

IEEE

[1]K. U. Birant, “Software Maintainability Index Prediction with Source Code Metrics using Machine Learning”, AITA, vol. 6, no. 1, pp. 44–55, May 2026, [Online]. Available: https://izlik.org/JA27FG86YP

ISNAD

Birant, Kökten Ulaş. “Software Maintainability Index Prediction With Source Code Metrics Using Machine Learning”. Artificial Intelligence Theory and Applications 6/1 (May 1, 2026): 44-55. https://izlik.org/JA27FG86YP.

JAMA

1.Birant KU. Software Maintainability Index Prediction with Source Code Metrics using Machine Learning. AITA. 2026;6:44–55.

MLA

Birant, Kökten Ulaş. “Software Maintainability Index Prediction With Source Code Metrics Using Machine Learning”. Artificial Intelligence Theory and Applications, vol. 6, no. 1, May 2026, pp. 44-55, https://izlik.org/JA27FG86YP.

Vancouver

1.Kökten Ulaş Birant. Software Maintainability Index Prediction with Source Code Metrics using Machine Learning. AITA [Internet]. 2026 May 1;6(1):44-55. Available from: https://izlik.org/JA27FG86YP