Academic Journal
A STUDY OF ENSEMBLE MODELS FOR DEFECT PREDICTION FROM CLASS DIAGRAM.
| Title: | A STUDY OF ENSEMBLE MODELS FOR DEFECT PREDICTION FROM CLASS DIAGRAM. |
|---|---|
| Authors: | BATTULGA, Batnyam, TSOODOL, Lkhamrolom, ERDENEBAATAR, Bilguun, ERDENEBAATAR, Tsetsegjargal, NAMSRAI, Oyun-Erdene, BOLD, Naranchimeg |
| Source: | Advances in Electrical & Electronic Engineering; Jun2026, Vol. 24 Issue 2, p91-99, 9p |
| Subject Terms: | Ensemble learning, Unified modeling language, Computer software development, Defect tracking (Computer software development), Machine learning |
| Abstract: | Software defect prediction in the early stages of the Software Development Life Cycle (SDLC) is crucial to reducing project cost and ensuring the implementation's success. Existing methods for software defect detection in a project rely on the implementation or testing phases of the SDLC, based on the source code. While relatively few studies have focused on identifying defects in the design phase of the SDLC, these approaches primarily employ machine learning or deep learning methods to detect and classify suspect code segments or classes in static diagrams as defective or clean. This study utilizes 24 model-based metrics extracted via SDMetrics, including structural and objectoriented design features derived from UML class diagrams. To enhance classification performance, this study introduces an ensemble machine learning model with different techniques (stacking, voting) that combine multiple machine learning models. Specifically, we compare ensemble models with different ensemble techniques to the individual models in terms of accuracy, precision, recall, F-measure, and AUC by utilizing a large dataset called the Unified Bug Dataset, comprising five publicly available sub-datasets. Experimental results show that the ensemble model with the stacking ensemble method outperformed other ensemble models and the individual classifiers (RF, XGBoost, ET) in terms of AUC. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Electrical & Electronic Engineering is the property of Advances in Electrical & Electronic Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: A STUDY OF ENSEMBLE MODELS FOR DEFECT PREDICTION FROM CLASS DIAGRAM. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22BATTULGA%2C+Batnyam%22">BATTULGA, Batnyam</searchLink><br /><searchLink fieldCode="AR" term="%22TSOODOL%2C+Lkhamrolom%22">TSOODOL, Lkhamrolom</searchLink><br /><searchLink fieldCode="AR" term="%22ERDENEBAATAR%2C+Bilguun%22">ERDENEBAATAR, Bilguun</searchLink><br /><searchLink fieldCode="AR" term="%22ERDENEBAATAR%2C+Tsetsegjargal%22">ERDENEBAATAR, Tsetsegjargal</searchLink><br /><searchLink fieldCode="AR" term="%22NAMSRAI%2C+Oyun-Erdene%22">NAMSRAI, Oyun-Erdene</searchLink><br /><searchLink fieldCode="AR" term="%22BOLD%2C+Naranchimeg%22">BOLD, Naranchimeg</searchLink> – Name: TitleSource Label: Source Group: Src Data: Advances in Electrical & Electronic Engineering; Jun2026, Vol. 24 Issue 2, p91-99, 9p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Unified+modeling+language%22">Unified modeling language</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software defect prediction in the early stages of the Software Development Life Cycle (SDLC) is crucial to reducing project cost and ensuring the implementation's success. Existing methods for software defect detection in a project rely on the implementation or testing phases of the SDLC, based on the source code. While relatively few studies have focused on identifying defects in the design phase of the SDLC, these approaches primarily employ machine learning or deep learning methods to detect and classify suspect code segments or classes in static diagrams as defective or clean. This study utilizes 24 model-based metrics extracted via SDMetrics, including structural and objectoriented design features derived from UML class diagrams. To enhance classification performance, this study introduces an ensemble machine learning model with different techniques (stacking, voting) that combine multiple machine learning models. Specifically, we compare ensemble models with different ensemble techniques to the individual models in terms of accuracy, precision, recall, F-measure, and AUC by utilizing a large dataset called the Unified Bug Dataset, comprising five publicly available sub-datasets. Experimental results show that the ensemble model with the stacking ensemble method outperformed other ensemble models and the individual classifiers (RF, XGBoost, ET) in terms of AUC. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Advances in Electrical & Electronic Engineering is the property of Advances in Electrical & Electronic Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.15598/aeee.v24i2.250605 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 91 Subjects: – SubjectFull: Ensemble learning Type: general – SubjectFull: Unified modeling language Type: general – SubjectFull: Computer software development Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: A STUDY OF ENSEMBLE MODELS FOR DEFECT PREDICTION FROM CLASS DIAGRAM. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: BATTULGA, Batnyam – PersonEntity: Name: NameFull: TSOODOL, Lkhamrolom – PersonEntity: Name: NameFull: ERDENEBAATAR, Bilguun – PersonEntity: Name: NameFull: ERDENEBAATAR, Tsetsegjargal – PersonEntity: Name: NameFull: NAMSRAI, Oyun-Erdene – PersonEntity: Name: NameFull: BOLD, Naranchimeg IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13361376 Numbering: – Type: volume Value: 24 – Type: issue Value: 2 Titles: – TitleFull: Advances in Electrical & Electronic Engineering Type: main |
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