Share This Article:

Knowledge Management of Software Productivity and Development Time

Abstract Full-Text HTML Download Download as PDF (Size:236KB) PP. 609-618
DOI: 10.4236/jsea.2011.411072    3,962 Downloads   7,303 Views   Citations

ABSTRACT

In this paper, we identify a set of factors that may be used to forecast software productivity and software development time. Software productivity was measured in function points per person hours, and software development time was measured in number of elapsed days. Using field data on over 130 field software projects from various industries, we empirically test the impact of team size, integrated computer aided software engineering (ICASE) tools, software development type, software development platform, and programming language type on the software development productivity and development time. Our results indicate that team size, software development type, software development platform, and programming language type significantly impact software development productivity. However, only team size significantly impacts software development time. Our results indicate that effective management of software development teams, and using different management strategies for different software development type environments may improve software development productivity.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

J. Rodger, P. Pankaj and A. Nahouraii, "Knowledge Management of Software Productivity and Development Time," Journal of Software Engineering and Applications, Vol. 4 No. 11, 2011, pp. 609-618. doi: 10.4236/jsea.2011.411072.

References

[1] R. L. Glass, “The Realities of Software Technology Pay-offs,” Communications of the ACM, Vol. 42, No. 2, 1999, pp. 74-79. doi:10.1145/293411.293481
[2] J. D. Blackburn, G. D. Scudder and L. N. Van Wassenhove, “Improving Speed and Productivity of Software Development: A Global Survey of Software Developers,” IEEE Transactions on Software Engineering, Vol. 22, No. 12, 1996, pp. 875-885. doi:10.1109/32.553636
[3] R. Banker and S. A. Slaughter, “A Field Study of Scale Economies in Software Maintenance,” Management Science, Vol. 43, No. 12, 1997, pp. 1709-1725. doi:10.1287/mnsc.43.12.1709
[4] J. Blackburn, G. Scudder, L. Van Wassenhove and C. Hill, “Time Based Software Development,” Integrated Manufacturing Systems, Vol. 7, No. 2, 1996a, pp. 35-45. doi:10.1108/09576069610111918
[5] L. Fried, “Team Size and Productivity in Systems Development,” Journal of Information Systems Management, Vol. 8, No. 3, 1991, pp. 27-41. doi:10.1080/07399019108964994
[6] P. C. Pendharkar and J. A. Rodger, “A Probabilistic Model and a Belief Updating Procedure for Predicting Software Development Effort,” IEEE Transactions on Software Engineering, Vol. 31, No. 7, 2005, pp. 615-624. doi:10.1109/TSE.2005.75
[7] L. Angelis, I. Stamelos and M. Morisio, “Building a Software Cost Estimation Model Based on Categorical Data,” Proceedings of Seventh International Software Metrics Symposium, London, UK, 2001, pp. 4-15.
[8] C. J. Lokan, “An Empirical Analysis of Function Point Adjustment Factors,” Information and Software Technology, Vol. 42, 2000, pp. 649-660. doi:10.1016/S0950-5849(00)00108-7
[9] I. Stamelos, L. Angelis, M. Morisio, E. Sakellaris and G. L. Bleris, “Estimating the Development Cost of Custom Software,” Information & Management, Vol. 40, 2003, pp. 729-741. doi:10.1016/S0378-7206(02)00099-X
[10] G. H. Subramanian and G. E. Zarnich, “An Examination of Some Software Development Effort and Productivity Determinants in ICASE Tool Projects,” Journal of Management Information Systems, Vol. 12, No. 14, 1996, pp. 143-160.
[11] W. B. Foss, “Fast, Faster, Fastest Development,” Computerworld, Vol. 27, No. 22, 1993, pp. 81-83.
[12] R. K. Smith, J. F. Hale and A. S. Parrish, “An Empirical Study Using Task Assignment Patterns to Improve the Accuracy of Software Effort Estimation,” IEEE Transactions on Software Engineering, Vol. 27, No. 3, 2001. doi:10.1109/32.910861
[13] C. D. Wrigley and A. S. Dexter, “A Model of Measuring Information System Size,” MIS Quarterly, Vol. 15, No. 2, 1991, pp. 245-257. doi:10.2307/249386
[14] W. Gregory and W. Wojtkowski, “Applications Software Programming with Fourth-Generation Languages,” Boyd and Fraser, Boston, 1990.
[15] M. A. Cusumano and C. E. Kemerer, “A Quantitative Analysis of U.S. and Japanese Practice and Performance in Software Development,” Management Science, Vol. 16, No. 11, 1990, pp. 1384-1406. doi:10.1287/mnsc.36.11.1384
[16] G. Gamota and W. Frieman, “Gaining Ground: Japan’s Strides in Science and Technology,” Ballinger, Cambridge, 1988.
[17] C. Johnson, “Software in Japan,” Electronic Engineering Times, 1985, p. 1.
[18] M. V. Zelkowitz, et al., “Software Engineering Practices in the U.S. and Japan,” IEEE Computer, 1984, pp. 57-66.
[19] P. Mimno, “Power to the Users,” Computerworld, 1985, pp. 11-28.
[20] R. D. Banker and R. J. Kauffman, “Reuse and Productivity in Integrated Computer-Aided Software Engineering: An Empirical Study,” MIS Quarterly, Vol. 15, No. 3, 1991, pp. 375-401. doi:10.2307/249649
[21] I. Vessey, S. L. Jarvenpaa and N. Tractinsky, “Evaluation of Vendor Products: CASE Tools as Methodology Companions,” Communications of the ACM, Vol. 35, No. 4, 1992, pp. 90-105. doi:10.1145/129852.129860
[22] G. R. Finne, G. E. Witting and J. M. Dersharnais, “Estimation Software Development Effort with Case-Based Reasoning,” The 2nd International Conference on Case- Based Reasoning (ICCBR-97), Providence, 1997, pp. 13-32.
[23] T. E. Hastings and A. S. Sajeev, “A Vector-Based Approach to Software Size Measurement and Effort Estimation.” IEEE Transactions on Software Engineering, Vol. 27, No. 4, 2001, pp. 337-350. doi:10.1109/32.917523
[24] E. S. June and J. K. Lee, “Quasi-Optimal Case Selective Neural Network Model for Software Effort Estimation,” Expert Systems with Application, Vol. 21, 2001, pp. 1-14. doi:10.1016/S0957-4174(01)00021-5
[25] K. Sengupta, T. K. Abdel-Hamid and M. Bosley, “Coping with Staffing Delays in Software Project Management: An Experimental Investigation,” IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, Vol. 29, No. 1, 1999, pp. 77-91. doi:10.1109/3468.736362
[26] C. F. Kemerer, “Progress, Obstacles, and Opportunities in Software Engineering Economics,” Communications of the ACM, Vol. 41, No. 8, 1998, pp. 63-66. doi:10.1145/280324.280334
[27] P. C. Pendharkar and J. A. Rodger, “An Empirical Study of the Impact of Team Size on Software Development Effort,” Information Technology and Management, 2007.
[28] P. C. Pendharkar and J. A. Rodger, “The Relationship between Software Development Team Size and Software Development Cost,” Communications of the ACM, forthcoming, 2007.
[29] R. D. Banker and C. F. Kemerer, “Scale Economies in New Software Development,” IEEE Transactions on Software Engineering, Vol. 15, No. 10, 1989, pp. 1199-1205. doi:10.1145/280324.280334
[30] A. J. Albrecht and J. E. Gaffney, “Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation,” IEEE Transaction on Software Engineering, Vol. 6, 1983, pp. 639-647. doi:10.1109/TSE.1983.235271
[31] B. W. Boehm, B. Clar, C. Horowitz, C. Westland, R. Madachy and R. Selby, “Cost Models for Future Software Life Cycle Processes: COCOMO 2.0.0,” Annals of Software Engineering, Vol. 1, No. 1, 1995, pp. 1-30. doi:10.1007/BF02249046
[32] L. Greiner, “Is Development a Slave to the Platform?” Computing Canada, Vol. 30, No. 17, 2004, p. 18.
[33] G. B. Shelly, T. J. Cashman and H. J. Rosenblatt, “Systems Analysis and Design,” 7th Edition, Boston: Thompson-Course Technology.
[34] R. M. Stair and G. W. Reynolds, “Principles of Information Systems,” Thomson-Course Technology, New York, 2003.
[35] R. D. Banker and S. Slaughter, “The Moderating Effects of Structure on Volatility and Complexity in Software Enhancement,” Information Systems Research, Vol. 11, No. 3, 2000, pp. 219-240. doi:10.1287/isre.11.3.219.12209
[36] B. Unhelkar, “Process Quality Assurance for UML-Based Projects,” Addison Wesley, Boston, 2003.
[37] G. Butchner, “Addressing Software Volatility in the System Life Cycle,” Unpublished Doctoral Thesis, 1997.
[38] J. A. Hager, “Software Cost Reduction Methods in Practice: A Post-Mortem Analysis,” Journal of Systems and Software, Vol. 14, No. 2, 1991, pp. 67-77. doi:10.1016/0164-1212(91)90091-J
[39] B. Littlewood and L. Strigini, “Validation of Ultrahigh Dependability for Software-Based Systems,” Communications of the ACM, Vol. 36, No. 11, 1993, pp. 69-80. doi:10.1145/163359.163373
[40] B. G. Tabachnick and L. S. Fidell, “Using Multivariate Statistics,” Needham Heights, MA: Allyn and Bacon, 2001.
[41] C. F. Kemerer, “How the Learning Curve Affects CASE Tool Adoption,” IEEE Software, 1992, pp. 23-28. doi:10.1109/52.136161

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.