Academic Journal

ArchSense: Characterizing Component-Internal Implementation-Semantic Evolution via Code Representation.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: ArchSense: Characterizing Component-Internal Implementation-Semantic Evolution via Code Representation.
Συγγραφείς: Wang, Tong, Jing, Guowei, Zhou, Hao
Πηγή: Electronics (2079-9292); Jul2026, Vol. 15 Issue 13, p2858, 31p
Θεματικοί όροι: Software architecture, Encoding, Computer software quality control, Java programming language
Περίληψη: Monitoring software architecture evolution is essential for maintaining system quality, but many practical change-detection pipelines still rely on syntactic differences. Such pipelines can produce large review streams in which minor edits, refactorings, and deeper implementation rewrites are mixed together. In this paper, we introduce ArchSense, a diagnostic framework for characterizing component-internal implementation-semantic evolution. ArchSense combines hierarchical structural alignment with method-level semantic displacement measurement. Instead of treating all code updates as equal, it uses vector representations from GraphCodeBERT to measure implementation-level semantic change intensity and then summarizes this signal within architectural components. We evaluate ArchSense on six large-scale open-source Java systems. The results show that semantic distance is positively associated with a three-annotator construct assessment of implementation change intensity. Additional disagreement analysis links high and low model decisions to concrete source code features, including control flow, call-set, return, exception, synchronization, and test-observable changes. A behavioral proxy check on 23,389 test method updates shows that the default threshold behaves as a conservative high-precision, low-recall review signal for implementation-semantic change. The validated scope of ArchSense is therefore component-internal diagnostic characterization for architecture-oriented review; connector topology, runtime communication, behavioral equivalence, and defect causality require complementary evidence. [ABSTRACT FROM AUTHOR]
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. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: ArchSense: Characterizing Component-Internal Implementation-Semantic Evolution via Code Representation.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Tong%22">Wang, Tong</searchLink><br /><searchLink fieldCode="AR" term="%22Jing%2C+Guowei%22">Jing, Guowei</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Hao%22">Zhou, Hao</searchLink>
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  Data: Electronics (2079-9292); Jul2026, Vol. 15 Issue 13, p2858, 31p
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  Data: <searchLink fieldCode="DE" term="%22Software+architecture%22">Software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Encoding%22">Encoding</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+quality+control%22">Computer software quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Java+programming+language%22">Java programming language</searchLink>
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  Data: Monitoring software architecture evolution is essential for maintaining system quality, but many practical change-detection pipelines still rely on syntactic differences. Such pipelines can produce large review streams in which minor edits, refactorings, and deeper implementation rewrites are mixed together. In this paper, we introduce ArchSense, a diagnostic framework for characterizing component-internal implementation-semantic evolution. ArchSense combines hierarchical structural alignment with method-level semantic displacement measurement. Instead of treating all code updates as equal, it uses vector representations from GraphCodeBERT to measure implementation-level semantic change intensity and then summarizes this signal within architectural components. We evaluate ArchSense on six large-scale open-source Java systems. The results show that semantic distance is positively associated with a three-annotator construct assessment of implementation change intensity. Additional disagreement analysis links high and low model decisions to concrete source code features, including control flow, call-set, return, exception, synchronization, and test-observable changes. A behavioral proxy check on 23,389 test method updates shows that the default threshold behaves as a conservative high-precision, low-recall review signal for implementation-semantic change. The validated scope of ArchSense is therefore component-internal diagnostic characterization for architecture-oriented review; connector topology, runtime communication, behavioral equivalence, and defect causality require complementary evidence. [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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        Value: 10.3390/electronics15132858
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        Text: English
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      – SubjectFull: Computer software quality control
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      – TitleFull: ArchSense: Characterizing Component-Internal Implementation-Semantic Evolution via Code Representation.
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              Text: Jul2026
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              Y: 2026
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