This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
When Will It Be Done? Machine Learner Answers to the 300-Billion-Dollar Question
May/June 2003 (vol. 18 no. 3)
pp. 48-50
Gary D. Boetticher, University of Houston-Clear Lake

Corporations invest over 300 billion dollars annually in software production. A key question in the software development process is, When will it be done? Estimating techniques include human-based (expert and analogy), algorithmic (Function Point Analysis and Cocomo [Cost Constructive Model], and machine learner-based. Human-based techniques are the most popular. However, machine learner-based techniques have generated impressive results, including accuracy rates within 25 percent, 83 percent of the time in the software life cycle's early stages. This article presents details from three machine learner successes in software effort estimation.

Index Terms:
machine learners, software engineering, project estimation, neural networks, backpropagation, software metrics, Bayesian analysis, effort estimation, programming effort
Citation:
Gary D. Boetticher, "When Will It Be Done? Machine Learner Answers to the 300-Billion-Dollar Question," IEEE Intelligent Systems, vol. 18, no. 3, pp. 48-50, May-June 2003, doi:10.1109/MIS.2003.1200728
Usage of this product signifies your acceptance of the Terms of Use.