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Software cost estimation technique based on bagging ensemble learning algorithm | ||
| Al-Qadisiyah Journal for Engineering Sciences | ||
| Article 4, Volume 19, Issue 2, June 2026, Pages 188-194 PDF (7.58 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.30772/qjes.2024.147128.1138 | ||
| Authors | ||
| Nedaa T. Qassem; Ibrahim Ahmed Saleh* | ||
| Software Department, College of Computer & Math, University of Mosul, Iraq. | ||
| Abstract | ||
| Recently, software cost estimation has become a more important issue in the software project development cycle, software quality, and decision-making management. In view of the common problem of inaccurate and difficult cost estimation in the software industry, in this article, the proposed bagging method is one of the ensemble learning methods to estimate the cost of software development. Five algorithms were used as the basic models: Random Forest, Decision Tree, AdaBoost, K-Nearest Neighbor, and Gradient Boosting, compiled using the bagging method. The proposed method was applied to a data set (ISBSG). The contribution of the paper suggests a more accurate method compared to previous studies, and applying it to high-quality data, which was prepared to obtain more accurate results when applying the proposed model, which showed its superiority over individual models in estimating the cost of software development. The results showed high accuracy in R2 prediction in ratio (97%) and gave a lower error rate (MMRE: Mean Magnitude of Relative Error) compared to previous studies in ratio (0.1). This indicates its accuracy in prediction is closer to the real cost, where the RF model was the basic estimator model in this method, because it surpasses the main models that were used in the proposed method. The KNN model gave the lowest accuracy ratio (73%) among the main models when trained on the ISBSG dataset. | ||
| Keywords | ||
| Ada boost; Decision tree; Gradient boosting; ISBSG dataset; Machine learning; Random Forest | ||
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