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Deep code search efficiency based on clustering.

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Titel: Deep code search efficiency based on clustering.
Autoren: Liu, Kun, Liu, Jianxun, Hu, Haize
Quelle: Concurrency & Computation: Practice & Experience; 6/10/2024, Vol. 36 Issue 13, p1-15, 15p
Schlagwörter: K-means clustering, COMPUTER software development, DEEP learning, SQL
Abstract: The deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C‐DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C‐DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C‐DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K‐Means algorithm to improve the code search efficiency of C‐DCS further, reducing the search time to 93.8% of the baseline model. Hence, C‐DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development. [ABSTRACT FROM AUTHOR]
Copyright of Concurrency & Computation: Practice & Experience is the property of Wiley-Blackwell 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.)
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  Data: Deep code search efficiency based on clustering.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Kun%22">Liu, Kun</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Jianxun%22">Liu, Jianxun</searchLink><br /><searchLink fieldCode="AR" term="%22Hu%2C+Haize%22">Hu, Haize</searchLink>
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  Data: Concurrency & Computation: Practice & Experience; 6/10/2024, Vol. 36 Issue 13, p1-15, 15p
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  Data: <searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+software+development%22">COMPUTER software development</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22SQL%22">SQL</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C‐DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C‐DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C‐DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K‐Means algorithm to improve the code search efficiency of C‐DCS further, reducing the search time to 93.8% of the baseline model. Hence, C‐DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Concurrency & Computation: Practice & Experience is the property of Wiley-Blackwell 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: 10.1002/cpe.8027
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        Text: English
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      – SubjectFull: K-means clustering
        Type: general
      – SubjectFull: COMPUTER software development
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      – SubjectFull: DEEP learning
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      – SubjectFull: SQL
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      – TitleFull: Deep code search efficiency based on clustering.
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            NameFull: Liu, Kun
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              Text: 6/10/2024
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              Y: 2024
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              Value: 36
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