Quantifying IT estimation risks

Science of Computer Programming

Volumn 74, Issue 11-12, 2009, Pages 900-933

  Kulk, G P ^a   Peters, R J ^a   Verhoef, C ^a  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

Author keywords

Cost estimation; Cost overrun; Cost underrun; Data plausibility; Data quality; EQF; Forecasting errors; IT enabled business investment; Lift chart; Logistic regression; Maturity mismatch; Misestimation; Quantifying IT risks; Risk drivers; Risk factors

Indexed keywords

COST ESTIMATION; COST OVERRUN; DATA PLAUSIBILITY; DATA QUALITY; EQF; FORECASTING ERRORS; IT-ENABLED BUSINESS INVESTMENT; LOGISTIC REGRESSION; MATURITY MISMATCH; MISESTIMATION; QUANTIFYING IT RISKS; RISK DRIVERS; RISK FACTORS;

AUTOMOBILE DRIVERS; BENCHMARKING; COST ACCOUNTING; COSTS; ERRORS; ESTIMATION; LOGISTICS; PROJECT MANAGEMENT; REGRESSION ANALYSIS; RISK ANALYSIS; RISK MANAGEMENT; RISK PERCEPTION;

COST BENEFIT ANALYSIS;

EID: 70449115569     PISSN: 01676423     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.scico.2009.09.001     Document Type: Article

References (56)

1. A.J. Albrecht, Measuring application development productivity, in: Proceedings of the Joint SHARE/GUIDE/IBM Application Development Symposium, 1979, pp. 83-92
2. Albrecht A.J., and Gaffney J.E. Software function, source lines of code,and development effort prediction: A software science validation. IEEE Transactions on Software Engineering 9 9 (1983) 639-648
3. Allenby G.M., and Lenk P.J. Modeling household purchase behavior with logistic normal regression. Journal of the American Statistical Association 89 (1994)
4. Atkinson K. Elementary Numerical Analysis (1985), John Wiley & Sons
5. Bellman S., Lohse G.~L., and Johnson E.~J. Predictors of online buying behavior. Communications of the ACM 42 12 (1999) 32-38
6. S. Berinato, Using project portfolio management to demonstrate IT value, CIO Magazine, October 2001. Available via: www.cio.com/archive/100101/math.html
7. Braha D., and Bar-Yam Y. Topology of large-scale engineering problem-solving networks. Physical Review E 69 1 (2004)
8. Braha D., and Bar-Yam Y. The statistical mechanics of complex product development: Empirical and analytical results. Management Science 53 7 (2007) 1127-1145
9. Brand R. Using logistic regression in perinatal epidemiology: An introduction for clinical researchers. Part 2: The logistic regression equation. Paediatric and Perinatal Epidemiology 4 (1990) 234-249
10. Brand R., and Keirse M.J.N.C. Using logistic regression in perinatal epidemiology: An introduction for clinical researchers. Part 1: Basic concepts. Paediatric and Perinatal Epidemiology 4 (1990) 22-38
11. Brooks Jr. F.P. The Mythical Man-Month - Essays on Software Engineering. Anniversary Edition (1995), Addison-Wesley
12. S. le Cessie, Model building techniques for logistic regression, with applications to medical data. Ph.D. Thesis, University of Leiden, 1991
13. Charvat J. Project Management Methodologies (2003), Wiley
14. Timothy A. Chick, CMM/CMMI level 3 or higher? No guarantee for success. Defense AT&L, November-December 2006
15. 2 test of goodness of fit. The Annals of Mathematical Statistics 23 3 (1952) 315-345
16. 2 tests. Biometrics 10 4 (1954) 417-451
17. Conte S.D., Dunsmore H.E., and Shen V.Y. Software Engineering Metrics and Models (1986), Benjamin-Cummings Publishing Co., Inc.
18. Dalcher D., and Genus A. Introduction: Avoiding IS/IT implementation failure. Technology Analysis and Strategic Management 15 4 (2003) 403-407
19. Denaro G., Pezzè M., and Morasca S. Towards industrially relevant fault-proneness models. International Journal of Software Engineering and Knowledge Engineering 13 4 (2003) 395-417
20. J.L. Eveleens, C. Verhoef, Quantifying IT forecast quality. Accepted with minor revision, available via: http://www.cs.vu.nl/~x/cone/cone.pdf
21. Garmus D., and Herron D. Function Point Analysis-Measurement Practices for Successful Software Projects (2001), Addison-Wesley
22. The Standish Group, CHAOS, 1995
23. The Standish Group, CHAOS: A Recipe for Success, 1999
24. The Standish Group, EXTREME CHAOS, 2001
25. Hosmer D., and Lemeshow S. Applied logistic regression. Probability and Statistics. 2nd edition (2000), John Wiley & Sons, Inc.
26. Hosmer D.W., Hosmer T., Le Cessie S., and Lemeshow S. A comparison of goodness-of-fit test for the logistic regression model. Statistics in Medicine 16 (1997) 965-980
27. van Houwelingen J.C., and le Cessie S. Predictive value of statistical models. Statistics in Medicine 9 11 (1990) 1303-1325
28. Jackson J. Data mining: A conceptual overview. Communications of the Association for Information Systems 8 19 (2002)
29. Jones C. Patterns of Software Systems Failure and Success (1996), International Thomsom Computer Press
30. Jones C. Software Assessments, Benchmarks, and Best Practices. Information Technology Series (2000), Addison-Wesley
31. Khoshgoftaar T.M., Allena E.B., Jones W.D., and Hudepohl J.P. Accuracy of software quality models over multiple releases. Annals of Software Engineering 9 1-4 (2000) 103-116
32. Kulk G.P., and Verhoef C. Quantifying requirements volatility effects. Science of Computer Programming 72 3 (2008) 136-175. http://www.cs.vu.nl/~x/qrv/qrv.pdf Available via: http://www.cs.vu.nl/~x/qrv/qrv.pdf
33. Likert R. A technique for the measurement of attitudes. Archives of Psychology 140 (1932) 1-55
34. McFarlan F.W. Portfolio approach to information systems. Harvard Business Review 59 5 (1981) 142-150
35. I. Newton, Methodus fluxionum et serierum infinitarum, Londoni, 1671. English translation by John Colson 1736
36. Paliwala M., and Kumar U.A. Neural networks and statistical techniques: A review of applications. Expert Systems with Applications 36 (2009) 2-17
37. Paulk M.C., Curtis B., Chrissis M.B., and Weber C.V. Capability maturity model, version 1.1. IEEE Software 10 4 (1993) 18-27
38. Peduzzi P., Concato J., Kemper E., Holford T.R., and Feinstein A.R. A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology 49 (1996) 1372-1379
39. Peters R.J., and Verhoef C. Quantifying the yield of risk-bearing IT-portfolios. Science of Computer Programming 71 (2007) 17-56. http://www.cs.vu.nl/~x/yield/yield.pdf Available via: http://www.cs.vu.nl/~x/yield/yield.pdf
40. Procaccino J.D., Verner J.M., Overmyer S.P., and Darter M.E. Case study: Factors for early prediction of software development success. Information and Software Technology 44 1 (2002) 53-62
41. R Development Core Team, R: A Programming Environment for Data Analysis and Graphics. R Foundation for Statistical Computing, Vienna, Austria, 2.7.1 - 2008. ISBN 3-900051-12-7
42. Raphson J. Analysis aequationum universalis (1690), Londini
43. Schmidt R., Lyytinen K., Keil M., and Cule P. Identifying software project risks: An international Delphi study. Journal of Management Information Systems 17 4 (2001) 5-36
44. Schneidewind N.F. Investigation of logistic regression as a discriminant of software quality. Seventh International Symposium on Software Metrics (2001), IEEE Computer Society 328-337
45. Smith A. An Inquiry into the Nature and Causes of the Wealth of Nations. In: Soares S.M. (Ed). MetaLibri Digital Library vol. 1776 (2007)
46. C. Verhoef, Getting on top of IT, 2002. Available via: http://www.cs.vu.nl/~x/top/top.pdf
47. Verhoef C. Quantitative IT portfolio management. Science of Computer Programming 45 1 (2002) 1-96. http://www.cs.vu.nl/~x/ipm/ipm.pdf Available via: http://www.cs.vu.nl/~x/ipm/ipm.pdf
48. C. Verhoef, Quantifying software process improvement, 2004. Available via: http://www.cs.vu.nl/~x/spi/spi.pdf
49. Verhoef C. Quantifying the value of IT-investments. Science of Computer Programming 56 3 (2005) 315-342. http://www.cs.vu.nl/~x/val/val.pdf Available via: http://www.cs.vu.nl/~x/val/val.pdf
50. Verhoef C. Quantitative aspects of outsourcing deals. Science of Computer Programming 56 3 (2005) 275-313. http://www.cs.vu.nl/~x/out/out.pdf Available via: http://www.cs.vu.nl/~x/out/out.pdf
51. Verhoef C. Quantifying the effects of IT-governance rules. Science of Computer Programming 67 2-3 (2007) 247-277. http://www.cs.vu.nl/~x/gov/gov.pdf Available via: http://www.cs.vu.nl/~x/gov/gov.pdf
52. Verner J.M., Evanco W.M., and Cerpa N. State of the practice: An exploratory analysis of schedule estimation and software project success prediction. Information and Software Technology 49 2 (2007) 181-193
53. Weill P., and Broadbent M. Leveraging the New Infrastructure (1998), Harvard Business School Press
54. Eric W. Weisstein, Newton's method. Available at: http://mathworld.wolfram.com/NewtonsMethod.html
55. Witten I.H., and Frank E. Data Mining, Practical Machine Learning Tools and Techniques (2000), Elsevier
56. Zhang G.P., Keil M., Rai A., and Mann J. Predicting information technology project escalation: A neural network approach. Computing, Artificial Intelligence and Information Technology 146 1 (2003) 115-129