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SEMANTIC METRICS: METRICS BASED ON SEMANTIC ASPECTS OF SOFTWARE.

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Τίτλος: SEMANTIC METRICS: METRICS BASED ON SEMANTIC ASPECTS OF SOFTWARE.
Συγγραφείς: Stein, Cara1 (AUTHOR) cgall@itsc.uah.edu, Etzkorn, Letha2 (AUTHOR), Gholston, Sampson3 (AUTHOR), Farrington, Phillip3 (AUTHOR), Utley, Dawn3 (AUTHOR), Cox, Glenn2 (AUTHOR), Fortune, Julie3 (AUTHOR)
Πηγή: Applied Artificial Intelligence. Jan2009, Vol. 23 Issue 1, p44-77. 34p. 3 Diagrams, 10 Charts.
Θεματικοί όροι: *Computer software developers, *Electronic data processing personnel, *Computer industry personnel, *Computer software, *Computer systems, *Information theory, Electronic systems, Software measurement, Semantics
Περίληψη: Software practitioners need ways to assess their software, and metrics can provide an automated way to do that, providing valuable feedback with little effort earlier than the testing phase. Semantic metrics were proposed to quantify aspects of software quality based on the meaning of software's task in the domain. Unlike traditional software metrics, semantic metrics do not rely on code syntax. Instead, semantic metrics are calculated from domain information, using the knowledge base of a program understanding system. Because semantic metrics do not rely on code syntax, they can be calculated before code is fully implemented. This article evaluates the semantic metrics theoretically and empirically. We find that the semantic metrics compare well to existing metrics and show promise as early indicators of software quality. [ABSTRACT FROM AUTHOR]
Copyright of Applied Artificial Intelligence is the property of Taylor & Francis 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.)
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  Data: Software practitioners need ways to assess their software, and metrics can provide an automated way to do that, providing valuable feedback with little effort earlier than the testing phase. Semantic metrics were proposed to quantify aspects of software quality based on the meaning of software's task in the domain. Unlike traditional software metrics, semantic metrics do not rely on code syntax. Instead, semantic metrics are calculated from domain information, using the knowledge base of a program understanding system. Because semantic metrics do not rely on code syntax, they can be calculated before code is fully implemented. This article evaluates the semantic metrics theoretically and empirically. We find that the semantic metrics compare well to existing metrics and show promise as early indicators of software quality. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis 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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        Text: English
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        PageCount: 34
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      – SubjectFull: Semantics
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              Text: Jan2009
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              Y: 2009
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