Academic Journal
Implementation of Gated Graph Neural Network (GGNN) on Bug Prediction in Java, Python, and C++ Programming Languages.
| Τίτλος: | Implementation of Gated Graph Neural Network (GGNN) on Bug Prediction in Java, Python, and C++ Programming Languages. |
|---|---|
| Συγγραφείς: | Juwono, Elroy1 (AUTHOR), Novarino Phoa, Matthew Farrell1 (AUTHOR) matthew.phoa@binus.ac.id, Wijaya, Michael1 (AUTHOR), Suryaningrumi, Kristien Margi1 (AUTHOR), Siswanto, Ricky Reynardo1 (AUTHOR) |
| Πηγή: | Procedia Computer Science. 2025, Vol. 269, p1171-1180. 10p. |
| Θεματικοί όροι: | Graph neural networks, Java programming language, Python programming language, C++, Data structures, Defect tracking (Computer software development), Software failures, Machine learning |
| Περίληψη: | Software bug is a defect on a system which can lead to incorrect results or failure. It can make the development costs more wasteful. A software defect prediction can help the development process to find bugs and prioritize testing efforts. Traditional bug prediction like CVDP (Cross-Version Defect Prediction) approach rely mostly on structure-based and predefined patterns that struggle to capture complex interaction across source code. This can create gaps to accurately identify bugs that appear from deep relationships between elements. To this end, we need a better approach for bug prediction which can capture long dependencies and code context to be more details. Therefore we use the GNN (Graph Neural Network) approach, a type of neural network made to learn over graph-structured data. This approach represents code structure into a node and using messaging mechanism on every node to precisely capture relationships between code lines and complex details. GNN has many models, but in this paper we specifically use GGNN (Gated Graph Neural Network). GGNN has a GRU mechanism which can control the information gained from neighbour node and remove the unrelated information during messaging mechanism continuously. We use this model to predict bugs in multiple programming languages that have a different structure: Python, Java, and C++. The result is that our model gains a significant result than CVDP approach with F1-score 0.9079, loss value of 0.2919. precision of 0.9175, and recall of 0.9058. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Supplemental Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://www.doi.org/10.1016/j.procs.2025.09.058? Name: ScienceDirect (all content) (s7799221) Category: fullText Text: View record from ScienceDirect MouseOverText: View record from ScienceDirect |
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| Items | – Name: Title Label: Title Group: Ti Data: Implementation of Gated Graph Neural Network (GGNN) on Bug Prediction in Java, Python, and C++ Programming Languages. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Juwono%2C+Elroy%22">Juwono, Elroy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Novarino+Phoa%2C+Matthew+Farrell%22">Novarino Phoa, Matthew Farrell</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> matthew.phoa@binus.ac.id</i><br /><searchLink fieldCode="AR" term="%22Wijaya%2C+Michael%22">Wijaya, Michael</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Suryaningrumi%2C+Kristien+Margi%22">Suryaningrumi, Kristien Margi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Siswanto%2C+Ricky+Reynardo%22">Siswanto, Ricky Reynardo</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Procedia+Computer+Science%22">Procedia Computer Science</searchLink>. 2025, Vol. 269, p1171-1180. 10p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Java+programming+language%22">Java programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22C%2B%2B%22">C++</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Software+failures%22">Software failures</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software bug is a defect on a system which can lead to incorrect results or failure. It can make the development costs more wasteful. A software defect prediction can help the development process to find bugs and prioritize testing efforts. Traditional bug prediction like CVDP (Cross-Version Defect Prediction) approach rely mostly on structure-based and predefined patterns that struggle to capture complex interaction across source code. This can create gaps to accurately identify bugs that appear from deep relationships between elements. To this end, we need a better approach for bug prediction which can capture long dependencies and code context to be more details. Therefore we use the GNN (Graph Neural Network) approach, a type of neural network made to learn over graph-structured data. This approach represents code structure into a node and using messaging mechanism on every node to precisely capture relationships between code lines and complex details. GNN has many models, but in this paper we specifically use GGNN (Gated Graph Neural Network). GGNN has a GRU mechanism which can control the information gained from neighbour node and remove the unrelated information during messaging mechanism continuously. We use this model to predict bugs in multiple programming languages that have a different structure: Python, Java, and C++. The result is that our model gains a significant result than CVDP approach with F1-score 0.9079, loss value of 0.2919. precision of 0.9175, and recall of 0.9058. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.procs.2025.09.058 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1171 Subjects: – SubjectFull: Graph neural networks Type: general – SubjectFull: Java programming language Type: general – SubjectFull: Python programming language Type: general – SubjectFull: C++ Type: general – SubjectFull: Data structures Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Software failures Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Implementation of Gated Graph Neural Network (GGNN) on Bug Prediction in Java, Python, and C++ Programming Languages. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Juwono, Elroy – PersonEntity: Name: NameFull: Novarino Phoa, Matthew Farrell – PersonEntity: Name: NameFull: Wijaya, Michael – PersonEntity: Name: NameFull: Suryaningrumi, Kristien Margi – PersonEntity: Name: NameFull: Siswanto, Ricky Reynardo IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 10 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 18770509 Numbering: – Type: volume Value: 269 Titles: – TitleFull: Procedia Computer Science Type: main |
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