Software development cost estimation approaches – A survey

  • Authors:

  • Barry Boehm;Chris Abts;Sunita Chulani

  • Affiliations:

  • University of Southern California, Los Angeles, CA 90089‐0781, USA;University of Southern California, Los Angeles, CA 90089‐0781, USA;IBM Research, 650 Harry Road, San Jose, CA 95120, USA E‐mail: sunita@us.ibm.com

  • Venue:
  • Annals of Software Engineering
  • Year:
  • 2000

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Abstract

This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertise‐based techniques, learning‐oriented techniques, dynamics‐based models, regression‐based models, and composite‐Bayesian techniques for integrating expertise‐based and regression‐based models. Experience to date indicates that neural‐net and dynamics‐based techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates.