IIM  Vol.1 No.2 , November 2009
What can Software Engineers Learn from Manufacturing to Improve Software Process and Product?
Abstract: The purpose of this paper is to provide the software engineer with tools from the field of manufacturing as an aid to improving software process and product quality. Process involves classical manufacturing methods, such as statistical quality control applied to product testing, which is designed to monitor and correct the process when the process yields product quality that fails to meet specifications. Product quality is measured by metrics, such as failure count occurring on software during testing. When the process and product quality are out of control, we show what remedial action to take to bring both the process and product under control. NASA Space Shuttle failure data are used to illustrate the process methods.
Cite this paper: nullN. SCHNEIDEWIND, "What can Software Engineers Learn from Manufacturing to Improve Software Process and Product?," Intelligent Information Management, Vol. 1 No. 2, 2009, pp. 98-107. doi: 10.4236/iim.2009.12015.

[1]   J. G. Monks, “Operations management,” Second Edition, McGraw-Hill, 1996.

[2]   A. L. Jacob and S. K. Pillai, “Statistical process control to improve coding and code review,” IEEE Software, Vol. 20, No. 3, pp. 50–55, May/June, 2003.

[3]   T. Keller and N. F. Schneidewind, “A successful application of software reliability engineering for the NASA space shuttle,” Software Reliability Engineering Case Studies, International Symposium on Software Reliability Engineering, Albuquerque, New Mexico, November 4, pp. 71–82, 1997.

[4]   J. Q. Ning, “Component-based software engineering (CBSE),” 5th International Symposium on Assessment of Software Tools (SAST’97), p. 0034, 1997.

[5]   A. Sloane and W. Waite, “Issues in automatic software manufacturing in the presence of generators,” Australian Software Engineering Conference, p. 134, 1998.

[6]   Y. L. Yang, M. Li, and Y. Y. Huang, “The use of configuration conception in software development,” IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Vol. 2, pp. 963–967, 2008.

[7]   R. V. Binder, “Can a manufacturing quality model work for software?” IEEE Software, Vol. 14, No. 5, pp. 101– 102,105, September/October 1997.

[8]   M. Eiklenborg, S. Ioannou, G. King II, and M. Vilcheck, “Taguchi methods for achieving quality,” San Francisco State University, School of Engineering. http://userwww.

[9]   R. K. Roy, “Design of experiments using the taguchi approach: 16 steps to product and process improvement,” John Wiley & Sons, Inc., 2001.

[10]   G. Taguchi, S. Chowdhury, and Y. Wu, “Taguchi’s quality engineering handbook,” John Wiley & Sons, Inc., 2005.

[11]   Handbook of Software Reliability Engineering, Edited by Michael R. Lyu, Published by IEEE Computer Society Press and McGraw-Hill Book Company, 1996.

[12]   N. F. Schneidewind, “Reliability modeling for safety critical software,” IEEE Transactions on Reliability, Vol. 46, No. 1, pp. 88–98, March 1997.


[14]   N. Eickelmann and A. Anant, “Statistical process control: What you don’t measure can hurt you!” IEEE Software, Vol. 20, No. 2, pp. 49–51, March/April, 2003.

[15]   W. C. Turner, J. H. Mize, and J. W. Nazemetz, “Introduction to industrial and systems engineering,” Third Edition, Prentice Hall, 1993.

[16]   D. M. Levine, P. P. Ramsey, and R. K. Smidt, “Applied statistics for engineers and scientists,” Prentice-Hall, 2001.

[17]   J. D. Musa, A. Iannino, and K. Okumoto, “Software reliability: Measurement, prediction, application,” McGraw- Hill, 1987.

[18]   N. F. Fenton and S. L. Pfleeger, “Software metrics: A rigorous & practical approach,” Second Edition, PWS Publishing Company, 1997.