Ensemble Learning for Software Requirement-Risk Assessment: A Comparative Study of Bagging and Boosting Approaches

Chandan Kumar, Pathan Shaheen Khan, Medandrao Srinivas, Sudhanshu Kumar Jha, Shiv Prakash and Rajkumar Singh Rathore ()
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Chandan Kumar: School of Computing, Amrita Vishwa Vidyapeetham, Amaravati Campus, Agiripalli 522503, Andhra Pradesh, India
Pathan Shaheen Khan: School of Computing, Amrita Vishwa Vidyapeetham, Amaravati Campus, Agiripalli 522503, Andhra Pradesh, India
Medandrao Srinivas: Department of Data Science, NRI Institute of Technology, Agiripalli 521212, Andhra Pradesh, India
Sudhanshu Kumar Jha: Department of Electronics and Communication, University of Allahabad, Prayagraj 211002, Uttar Pradesh, India
Shiv Prakash: Department of Electronics and Communication, University of Allahabad, Prayagraj 211002, Uttar Pradesh, India
Rajkumar Singh Rathore: Department of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff Llandaff Campus, Cardiff CF5 2YB, UK

Future Internet, 2025, vol. 17, issue 9, 1-23

Abstract: In software development, software requirement engineering (SRE) is an essential stage that guarantees requirements are clear and unambiguous. However, incomplete inconsistency, and ambiguity in requirement documents often occur, which can cause project delay, cost escalation, or total failure. In response to these challenges, this paper introduces a machine learning method to automatically identify the risk levels of software requirements according to ensemble classification methods. The labeled textual requirement dataset was preprocessed utilizing conventional preprocessing techniques, label encoding, and oversampling with the synthetic minority oversampling technique (SMOTE) to handle class imbalance. Various ensemble and baseline models such as extra trees, random forest, bagging with decision trees, XGBoost, LightGBM, gradient boosting, decision trees, support vector machine, and multi-layer perceptron were trained and compared. Five-fold cross-validation was used to provide stable performance evaluation on accuracy, area under the ROC curve (AUC), F1-score, precision, recall, root mean square error (RMSE), and error rate. The bagging (DT) classifier achieved the best overall performance, with an accuracy of 99.55%, AUC of 0.9971 and an F1-score of 97.23%, while maintaining a low RMSE of 0.03 and error rate of 0.45%. These results demonstrate the effectiveness of ensemble-based classifiers, especially bagging (DT) classifiers, in accurately predicting high-risk software requirements. The proposed method enables early detection and mitigation of requirement risks, aiding project managers and software engineers in improving resource planning, reducing rework, and enhancing overall software quality.

Keywords: software requirement engineering; risk assessment; ensemble learning; bagging; boosting (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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