Academic Journal
JDQuery: Query-Driven Defect Localization for Java Source Code Based on Code Knowledge Graphs.
| Τίτλος: | JDQuery: Query-Driven Defect Localization for Java Source Code Based on Code Knowledge Graphs. |
|---|---|
| Συγγραφείς: | Hu, Tianyuan, Wang, Tong |
| Πηγή: | Electronics (2079-9292); Sep2026, Vol. 15 Issue 17, p3827, 17p |
| Θεματικοί όροι: | Java programming language, Knowledge graphs, Computer software quality control, Defect tracking (Computer software development), Software failures |
| Περίληψη: | Java is one of the most widely used object-oriented programming languages, making accurate and efficient defect localization essential for improving software quality and reliability. Conventional static analysis techniques primarily rely on predefined rules and localized syntactic matching, which may limit their ability to capture complex structural and semantic relationships among program entities. To address these limitations, this paper proposes JDQuery, a query-driven defect localization framework for Java source code based on a code knowledge graph. The framework parses Java source code into abstract syntax trees (ASTs), extracts software entities and their semantic relationships according to a formalized domain ontology, and constructs a unified code knowledge graph that integrates syntactic and semantic information. Based on the structural characteristics of Java defects, defect patterns are translated into Cypher queries, enabling flexible defect localization through graph pattern matching. Experiments on multiple open-source Java projects, including both injected defects and native real-world defects, demonstrate that JDQuery achieves precision values of 97.20% and 92.87% on two projects of different code sizes. A comparative evaluation with PMD further shows that JDQuery achieves substantially higher recall while maintaining comparable precision for the evaluated defects. Efficiency experiments demonstrate that JDQuery maintains millisecond-level query latency even when processing large-scale Java projects. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: JDQuery: Query-Driven Defect Localization for Java Source Code Based on Code Knowledge Graphs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hu%2C+Tianyuan%22">Hu, Tianyuan</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Tong%22">Wang, Tong</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electronics (2079-9292); Sep2026, Vol. 15 Issue 17, p3827, 17p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Java+programming+language%22">Java programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+quality+control%22">Computer software quality control</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> – Name: Abstract Label: Abstract Group: Ab Data: Java is one of the most widely used object-oriented programming languages, making accurate and efficient defect localization essential for improving software quality and reliability. Conventional static analysis techniques primarily rely on predefined rules and localized syntactic matching, which may limit their ability to capture complex structural and semantic relationships among program entities. To address these limitations, this paper proposes JDQuery, a query-driven defect localization framework for Java source code based on a code knowledge graph. The framework parses Java source code into abstract syntax trees (ASTs), extracts software entities and their semantic relationships according to a formalized domain ontology, and constructs a unified code knowledge graph that integrates syntactic and semantic information. Based on the structural characteristics of Java defects, defect patterns are translated into Cypher queries, enabling flexible defect localization through graph pattern matching. Experiments on multiple open-source Java projects, including both injected defects and native real-world defects, demonstrate that JDQuery achieves precision values of 97.20% and 92.87% on two projects of different code sizes. A comparative evaluation with PMD further shows that JDQuery achieves substantially higher recall while maintaining comparable precision for the evaluated defects. Efficiency experiments demonstrate that JDQuery maintains millisecond-level query latency even when processing large-scale Java projects. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI 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: 10.3390/electronics15173827 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 3827 Subjects: – SubjectFull: Java programming language Type: general – SubjectFull: Knowledge graphs Type: general – SubjectFull: Computer software quality control Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Software failures Type: general Titles: – TitleFull: JDQuery: Query-Driven Defect Localization for Java Source Code Based on Code Knowledge Graphs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hu, Tianyuan – PersonEntity: Name: NameFull: Wang, Tong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20799292 Numbering: – Type: volume Value: 15 – Type: issue Value: 17 Titles: – TitleFull: Electronics (2079-9292) Type: main |
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