Graph neural networks for precise bug localization through structural program analysis.

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Bibliographic Details
Title: Graph neural networks for precise bug localization through structural program analysis.
Authors: Yousofvand, Leila1, Soleimani, Seyfollah2, Rafe, Vahid3, vahid.rafe@city.ac.uk, Nikanjam, Amin4
Source: Automated Software Engineering; Jun2026, Vol. 33 Issue 1, p1-28, 28p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 188771513
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Graph neural networks for precise bug localization through structural program analysis.
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  Data: <searchLink fieldCode="AU" term="%22Yousofvand%2C+Leila%22">Yousofvand, Leila</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Soleimani%2C+Seyfollah%22">Soleimani, Seyfollah</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AU" term="%22Rafe%2C+Vahid%22">Rafe, Vahid</searchLink><relatesTo>3</relatesTo>, <i>vahid.rafe@city.ac.uk</i><br /><searchLink fieldCode="AU" term="%22Nikanjam%2C+Amin%22">Nikanjam, Amin</searchLink><relatesTo>4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Automated+Software+Engineering%22">Automated Software Engineering</searchLink>; Jun2026, Vol. 33 Issue 1, p1-28, 28p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=188771513
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10515-025-00556-y
    Languages:
      – Code: eng
        Text: English
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        PageCount: 28
        StartPage: 1
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      – TitleFull: Graph neural networks for precise bug localization through structural program analysis.
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          Name:
            NameFull: Yousofvand, Leila
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            NameFull: Soleimani, Seyfollah
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            NameFull: Rafe, Vahid
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            NameFull: Nikanjam, Amin
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 33
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