Software enhancement effort estimation using correlation-based feature selection and stacking ensemble method
| العنوان: | Software enhancement effort estimation using correlation-based feature selection and stacking ensemble method |
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| المؤلفون: | Asma Sellami, Nadia Bouassida, Zaineb Sakhrawi |
| المصدر: | Cluster Computing. 25:2779-2792 |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Mean squared error, Computer Networks and Communications, business.industry, Computer science, Feature selection, Regression analysis, Software maintenance, computer.software_genre, Ensemble learning, Software, Data mining, business, computer, Software project management, Data reduction |
| الوصف: | Estimating software enhancement efforts became a challenging task in software project management. Recent researches focused on identifying the best machine learning algorithms for software maintenance effort estimation. Most of the research publications investigated the use of ensemble learning for improving software effort estimation. Intending to increase the estimation accuracy over individual models, this paper investigates the use of the stacking ensemble method for estimating the enhancement maintenance effort (EME) of software projects. This paper makes a comparison between two machine learning-based approaches for estimating software EME: The M5P (as an individual model) and the stacking as an ensemble method combining different regression models (GBRegr, LinearSVR, and RFR) using the ISBSG dataset. A correlation-based feature selection (CFS) algorithm is basically used to achieve efficient data reduction. The selected ML techniques-based approaches were trained and tested on a dataset with relevant features leading to the improvement of estimate accuracy. Results show that the software EME estimation using CFS and stacking ensemble method is improved in terms of mean absolute error (MAE) = 0.0383 and root mean square error (RMSE) = 0.1973. |
| تدمد: | 1573-7543 1386-7857 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::3ef63f3cdc75293997d22c8ff5d56b82 https://doi.org/10.1007/s10586-021-03447-5 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........3ef63f3cdc75293997d22c8ff5d56b82 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 15737543 13867857 |
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