Abstract
Software maintainability is a prime trait of software, measured as the ease with which new code lines can be added, obsolete ones can be deleted, and those having errors can be corrected. The significance of software maintenance is increasing in today’s digital era leading to the use of advanced machine learning (ML) algorithms for building efficient models to predict maintainability, although several baseline ML algorithms are already in use for software maintainability prediction (SMP). However, in the current study, an effort has been made to improve the existing baseline models using hyperparameter tuning. Hyperparameter tuning chooses the best set of hyperparameters for an algorithm, where a hyperparameter is that parameter that uses its value for controlling the training process. This study employs default hyperparameter tuning as well as the grid search-based hyperparameter tuning. Five regression-based ML algorithms, i.e., Random Forest, Ridge Regression, Support Vector Regression, Stochastic Gradient Descent, and Gaussian Process Regression, have been implemented using two commercial object-oriented datasets, namely QUES and UIMS for SMP. To evaluate the performance, a comparison has been made between the baseline models and the models developed after hyperparameter tuning based on the three accuracy measures, viz., R-Squared, Mean Absolute Error (MAE), and Root Mean Squared Logarithmic Error (RMSLE). The results depict that the performance of all the five baseline ML algorithms improved after applying hyperparameter tuning. This conclusion is supported by the improved R-squared, MAE, and RMSLE values obtained in this study. Best results are obtained when the grid search method is used for the tuning purpose. On average, the values of R-squared, MAE, and RMSLE measures improved by 20.24%, 12.26%, and 30.28%, respectively, for the QUES dataset. On the other hand, in the case of the UIMS dataset, an average improvement of 6.27%, 15.71%, and 16.39% has been achieved in terms of R-squared, MAE, and RMSLE, respectively.
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Lakra, K., Chug, A. (2021). Improving Software Maintainability Prediction Using Hyperparameter Tuning of Baseline Machine Learning Algorithms. In: Choudhary, A., Agrawal, A.P., Logeswaran, R., Unhelkar, B. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 778. Springer, Singapore. https://doi.org/10.1007/978-981-16-3067-5_51
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DOI: https://doi.org/10.1007/978-981-16-3067-5_51
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