SEMANTIC METRICS: METRICS BASED ON SEMANTIC ASPECTS OF SOFTWARE

  • Authors:

  • Cara Stein;Letha Etzkorn;Sampson Gholston;Phillip Farrington;Dawn Utley;Glenn Cox;Julie Fortune

  • Affiliations:

  • Information Technology and Systems Center, University of Alabama in Huntsville, Huntsville, Alabama, USA;Computer Science Department, University of Alabama in Huntsville, Huntsville, Alabama, USA;Industrial and Systems Engineering Management Department, University of Alabama in Huntsville, Huntsville, Alabama, USA;Industrial and Systems Engineering Management Department, University of Alabama in Huntsville, Huntsville, Alabama, USA;Industrial and Systems Engineering Management Department, University of Alabama in Huntsville, Huntsville, Alabama, USA;Computer Science Department, University of Alabama in Huntsville, Huntsville, Alabama, USA;Industrial and Systems Engineering Management Department, University of Alabama in Huntsville, Huntsville, Alabama, USA

  • Venue:
  • Applied Artificial Intelligence
  • Year:
  • 2009

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Abstract

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.