Early estimation of defect density using an in-process Haskell metrics model

  • Authors:

  • Mark Sherriff;Nachiappan Nagappan;Laurie Williams;Mladen Vouk

  • Affiliations:

  • North Carolina State University, Raleigh, NC;Microsoft Research, Redmond, WA;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC

  • Venue:
  • A-MOST '05 Proceedings of the 1st international workshop on Advances in model-based testing
  • Year:
  • 2005

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Abstract

Early estimation of defect density of a product is an important step towards the remediation of the problem associated with affordably guiding corrective actions in the software development process. This paper presents a suite of in-process metrics that leverages the software testing effort to create a defect density prediction model for use throughout the software development process. A case study conducted with Galois Connections, Inc. in a Haskell programming environment indicates that the resulting defect density prediction is indicative of the actual system defect density.