Sunita Chulani, Member, IEEE, Barry Boehm, Fellow, IEEE, and Bert Steece

• S. Chulani is with IBM Research, Center for Software Engineering, 650 Harry Rd., San Jose, CA 95120. This work was performed while doing research at the Center for Software Engineering, University of Southern California, Los Angeles. E-mail: sunita@us.ibm.com.

• B. Boehm is with the Center for Software Engineering, University of Southern California, Los Angeles, CA 90089. E-mail: boehm@sunset.usc.edu.

• B. Steece is with the Marshall School of Business, University of Southern California, Los Angeles, CA 90089. E-mail: berts@almaak.usc.edu.

Manuscript received 29 June 1998; revised 25 Feb. 1999.

Recommended for acceptance by D. Ross Jeffery.

For information on obtaining reprints of this article, please send e-mail to: tse@computer.org , and reference IEEECS Log Number 109543.

Abstract To date many software engineering cost models have been developed to predict the cost, schedule, and quality of the software under development. But, the rapidly changing nature of software development has made it extremely difficult to develop empirical models that continue to yield high prediction accuracies. Software development costs continue to increase and practitioners continually express their concerns over their inability to accurately predict the costs involved. Thus, one of the most important objectives of the software engineering community has been to develop useful models that constructively explain the software development life-cycle and accurately predict the cost of developing a software product. To that end, many parametric software estimation models have evolved in the last...