Requirement Dependency Extraction Based on Improved Stacking Ensemble Machine Learning

Hui Guan (), Hang Xu and Lie Cai
Additional contact information
Hui Guan: Department of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110142, China
Hang Xu: Department of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110142, China
Lie Cai: Department of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110142, China

Mathematics, 2024, vol. 12, issue 9, 1-37

Abstract: To address the cost and efficiency issues of manually analysing requirement dependency in requirements engineering, a requirement dependency extraction method based on part-of-speech features and an improved stacking ensemble learning model (P-Stacking) is proposed. Firstly, to overcome the problem of singularity in the feature extraction process, this paper integrates part-of-speech features, TF-IDF features, and Word2Vec features during the feature selection stage. The particle swarm optimization algorithm is used to allocate weights to part-of-speech tags, which enhances the significance of crucial information in requirement texts. Secondly, to overcome the performance limitations of standalone machine learning models, an improved stacking model is proposed. The Low Correlation Algorithm and Grid Search Algorithms are utilized in P-stacking to automatically select the optimal combination of the base models, which reduces manual intervention and improves prediction performance. The experimental results show that compared with the method based on TF-IDF features, the highest F1 scores of a standalone machine learning model in the three datasets were improved by 3.89%, 10.68%, and 21.4%, respectively, after integrating part-of-speech features and Word2Vec features. Compared with the method based on a standalone machine learning model, the improved stacking ensemble machine learning model improved F1 scores by 2.29%, 5.18%, and 7.47% in the testing and evaluation of three datasets, respectively.

Keywords: requirement dependency; machine learning; part-of-speech features; particle swarm optimization; ensemble learning; low correlation algorithm; grid search algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/9/1272/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/9/1272/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:9:p:1272-:d:1380906

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().