Academic Journal
Software defect prediction at file level: A hybrid deep learning approach combining source code metrics and semantic features.
| Τίτλος: | Software defect prediction at file level: A hybrid deep learning approach combining source code metrics and semantic features. |
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| Συγγραφείς: | GEREMEW, Esrael, WAGAW, Mekonnen |
| Πηγή: | Romanian Journal of Information Technology & Automatic Control / Revista Română de Informatică și Automatică; 2026, Vol. 36 Issue 1, p77-90, 14p |
| Θεματικοί όροι: | Multilayer perceptrons, Long short-term memory, Software measurement, Programming language semantics, Machine learning, Defect tracking (Computer software development), Data structures, Deep learning |
| Περίληψη: | The necessity for trustworthy Software Defect Prediction (SDP) models is highlighted by the increasing complexity of contemporary software systems. These models facilitate the effective use of scarce testing resources by early detection of potentially defective modules. Although deep learning has demonstrated promise in learning characteristics from source code, the efficacy of current methods is generally limited by their reliance on a particular sort of information, such as hand-crafted code metrics or semantic features from code structure. One of the biggest challenges is still integrating several data types into a single, discriminative feature set. In order to forecast file-level defects, this study presents a unique approach that blends semantic characteristics with source code metrics. In addition to Combined Defect Data Modelling (CDDM) we suggest Learning Hybrid Feature Representation (LHFR), a deep neural network model. LHFR combines a Multi-Layer Perceptron (MLP) to learn from manually constructed metrics with a Bidirectional Long Short-Term Memory (Bi-LSTM) network to extract semantic features from Abstract Syntax Trees (ASTs). With an average F-measure of 69.08%, LHFR outperforms models based solely on metrics or semantic characteristics when tested on 12 open-source Java projects. A new combined dataset, an improved feature set and a hybrid representation strategy that significantly enhances fault detection performance are among the contributions. [ABSTRACT FROM AUTHOR] |
| Copyright of Romanian Journal of Information Technology & Automatic Control / Revista Română de Informatică și Automatică is the property of National Institute for Research & Development in Informatics - ICI Bucharest 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.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Software defect prediction at file level: A hybrid deep learning approach combining source code metrics and semantic features. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22GEREMEW%2C+Esrael%22">GEREMEW, Esrael</searchLink><br /><searchLink fieldCode="AR" term="%22WAGAW%2C+Mekonnen%22">WAGAW, Mekonnen</searchLink> – Name: TitleSource Label: Source Group: Src Data: Romanian Journal of Information Technology & Automatic Control / Revista Română de Informatică și Automatică; 2026, Vol. 36 Issue 1, p77-90, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Multilayer+perceptrons%22">Multilayer perceptrons</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Software+measurement%22">Software measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+language+semantics%22">Programming language semantics</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="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The necessity for trustworthy Software Defect Prediction (SDP) models is highlighted by the increasing complexity of contemporary software systems. These models facilitate the effective use of scarce testing resources by early detection of potentially defective modules. Although deep learning has demonstrated promise in learning characteristics from source code, the efficacy of current methods is generally limited by their reliance on a particular sort of information, such as hand-crafted code metrics or semantic features from code structure. One of the biggest challenges is still integrating several data types into a single, discriminative feature set. In order to forecast file-level defects, this study presents a unique approach that blends semantic characteristics with source code metrics. In addition to Combined Defect Data Modelling (CDDM) we suggest Learning Hybrid Feature Representation (LHFR), a deep neural network model. LHFR combines a Multi-Layer Perceptron (MLP) to learn from manually constructed metrics with a Bidirectional Long Short-Term Memory (Bi-LSTM) network to extract semantic features from Abstract Syntax Trees (ASTs). With an average F-measure of 69.08%, LHFR outperforms models based solely on metrics or semantic characteristics when tested on 12 open-source Java projects. A new combined dataset, an improved feature set and a hybrid representation strategy that significantly enhances fault detection performance are among the contributions. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Romanian Journal of Information Technology & Automatic Control / Revista Română de Informatică și Automatică is the property of National Institute for Research & Development in Informatics - ICI Bucharest 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: 10.33436/v36i1y202606 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 77 Subjects: – SubjectFull: Multilayer perceptrons Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Software measurement Type: general – SubjectFull: Programming language semantics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Data structures Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Software defect prediction at file level: A hybrid deep learning approach combining source code metrics and semantic features. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: GEREMEW, Esrael – PersonEntity: Name: NameFull: WAGAW, Mekonnen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 12201758 Numbering: – Type: volume Value: 36 – Type: issue Value: 1 Titles: – TitleFull: Romanian Journal of Information Technology & Automatic Control / Revista Română de Informatică și Automatică Type: main |
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