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Key words:multi-label learning; label complexity; feature complexity; sample complexity.

Bibliographic Details
Title: Key words:multi-label learning; label complexity; feature complexity; sample complexity. (English)
Authors: CHEN, Ran, SHI, Yaqing, HUANG, Song
Source: Journal of Computer Engineering & Applications; 2026, Vol. 62 Issue 11, p90-103, 14p
Subject Terms: Heuristic, Computer software testing, Automatic test equipment
Abstract: With the deep application of embedded systems in key fields, embedded software interface testing faces unique challenges different from general software testing due to the deep coupling between hardware and software, strict realtime constraints, and limited device resources. Through a systematic literature review, this paper first clarifies the definition and scope of embedded software interface testing, sorts out the types and core protocol characteristics of embedded software interfaces, and analyzes the difference between its testing process and general software interface testing. Then, it focuses on the evolution of embedded software interface testing technology, and summarizes the research status of automated testing frameworks, testing environment innovation and scenario-specific testing strategies. The paper focuses on the analysis of the core link of test data generation, and compares the advantages, disadvantages and application scenarios of model-based, program analysis-based and search heuristic-based methods. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)
Database: Complementary Index
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Header DbId: edb
DbLabel: Complementary Index
An: 194375919
RelevancyScore: 1082
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1082.4189453125
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Key words:multi-label learning; label complexity; feature complexity; sample complexity. (English)
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22CHEN%2C+Ran%22">CHEN, Ran</searchLink><br /><searchLink fieldCode="AR" term="%22SHI%2C+Yaqing%22">SHI, Yaqing</searchLink><br /><searchLink fieldCode="AR" term="%22HUANG%2C+Song%22">HUANG, Song</searchLink>
– Name: TitleSource
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  Data: Journal of Computer Engineering & Applications; 2026, Vol. 62 Issue 11, p90-103, 14p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+test+equipment%22">Automatic test equipment</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the deep application of embedded systems in key fields, embedded software interface testing faces unique challenges different from general software testing due to the deep coupling between hardware and software, strict realtime constraints, and limited device resources. Through a systematic literature review, this paper first clarifies the definition and scope of embedded software interface testing, sorts out the types and core protocol characteristics of embedded software interfaces, and analyzes the difference between its testing process and general software interface testing. Then, it focuses on the evolution of embedded software interface testing technology, and summarizes the research status of automated testing frameworks, testing environment innovation and scenario-specific testing strategies. The paper focuses on the analysis of the core link of test data generation, and compares the advantages, disadvantages and application scenarios of model-based, program analysis-based and search heuristic-based methods. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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: 10.3778/j.issn.1002-8331.2511-0253
    Languages:
      – Code: chi
        Text: Chinese
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 90
    Subjects:
      – SubjectFull: Heuristic
        Type: general
      – SubjectFull: Computer software testing
        Type: general
      – SubjectFull: Automatic test equipment
        Type: general
    Titles:
      – TitleFull: Key words:multi-label learning; label complexity; feature complexity; sample complexity.
        Type: main
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            NameFull: CHEN, Ran
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            NameFull: SHI, Yaqing
      – PersonEntity:
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            NameFull: HUANG, Song
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          Dates:
            – D: 01
              M: 06
              Text: 2026
              Type: published
              Y: 2026
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            – Type: volume
              Value: 62
            – Type: issue
              Value: 11
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            – TitleFull: Journal of Computer Engineering & Applications
              Type: main
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