Academic Journal
Software Defect Prediction Based on Multi-Hypergraph Adaptive Learning and Ensemble Learning.
| Τίτλος: | Software Defect Prediction Based on Multi-Hypergraph Adaptive Learning and Ensemble Learning. |
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| Συγγραφείς: | Liu, Weiguang1 weiguang.liu@zut.edu.cn, Ren, Junyan2 2023107340@zut.edu.cn, Zhang, Wenning3 zwn@zut.edu.cn |
| Πηγή: | IAENG International Journal of Computer Science. Mar2026, Vol. 53 Issue 3, p992-1006. 15p. |
| Θεματικοί όροι: | Ensemble learning, Graph neural networks, Machine learning, Defect tracking (Computer software development), Mathematical optimization |
| Περίληψη: | Software defect prediction plays a vital role in improving software quality and reliability. However, existing methods based on graph neural networks and hypergraph neural networks remain limited in modeling complex code dependency relations under multi-view feature settings. To address this limitation, we propose a novel multi-hypergraph neural network method, termed MHGNN-E, which constructs multiple view-specific hypergraphs from three complementary perspectives, including static code metrics, complex network metrics, and network embedding metrics, and learns representations via hypergraph convolution. Furthermore, we introduce an adaptive fusion mechanism with dynamic weighting to capture cross-view dependencies. In addition, to mitigate class imbalance, we employ a Bayesian-optimized ensemble classifier to fuse multiple base learners, which further improves prediction stability and generalization. Experiments on 29 PROMISE datasets show that MHGNN-E consistently improves key metrics, including AUC, F1, and MCC. Specifically, compared with the baselines, MHGNN-E improves AUC by 3.56% to 30.49%, F1 by 12.30% to 26.76%, and MCC by 29.07% to 81.98%. These results consistently support the effectiveness of the proposed multi-hypergraph adaptive learning and ensemble strategies for software defect prediction, and provide insights into hypergraph-based software defect prediction. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Supplemental Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edo&genre=article&issn=1819656X&ISBN=&volume=53&issue=3&date=20260301&spage=992&pages=992-1006&title=IAENG International Journal of Computer Science&atitle=Software%20Defect%20Prediction%20Based%20on%20Multi-Hypergraph%20Adaptive%20Learning%20and%20Ensemble%20Learning.&aulast=Liu%2C%20Weiguang&id=DOI: Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Software Defect Prediction Based on Multi-Hypergraph Adaptive Learning and Ensemble Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Weiguang%22">Liu, Weiguang</searchLink><relatesTo>1</relatesTo><i> weiguang.liu@zut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Ren%2C+Junyan%22">Ren, Junyan</searchLink><relatesTo>2</relatesTo><i> 2023107340@zut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Wenning%22">Zhang, Wenning</searchLink><relatesTo>3</relatesTo><i> zwn@zut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Mar2026, Vol. 53 Issue 3, p992-1006. 15p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software defect prediction plays a vital role in improving software quality and reliability. However, existing methods based on graph neural networks and hypergraph neural networks remain limited in modeling complex code dependency relations under multi-view feature settings. To address this limitation, we propose a novel multi-hypergraph neural network method, termed MHGNN-E, which constructs multiple view-specific hypergraphs from three complementary perspectives, including static code metrics, complex network metrics, and network embedding metrics, and learns representations via hypergraph convolution. Furthermore, we introduce an adaptive fusion mechanism with dynamic weighting to capture cross-view dependencies. In addition, to mitigate class imbalance, we employ a Bayesian-optimized ensemble classifier to fuse multiple base learners, which further improves prediction stability and generalization. Experiments on 29 PROMISE datasets show that MHGNN-E consistently improves key metrics, including AUC, F1, and MCC. Specifically, compared with the baselines, MHGNN-E improves AUC by 3.56% to 30.49%, F1 by 12.30% to 26.76%, and MCC by 29.07% to 81.98%. These results consistently support the effectiveness of the proposed multi-hypergraph adaptive learning and ensemble strategies for software defect prediction, and provide insights into hypergraph-based software defect prediction. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 992 Subjects: – SubjectFull: Ensemble learning Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: Software Defect Prediction Based on Multi-Hypergraph Adaptive Learning and Ensemble Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Weiguang – PersonEntity: Name: NameFull: Ren, Junyan – PersonEntity: Name: NameFull: Zhang, Wenning IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 3 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
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