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Issue No.03 - May/June (2008 vol.25)
pp: 24-28
Bill Curtis , CAST Software
Girish V. Seshagiri , Advanced Information Services
Donald Reifer , Reifer Consultants
Iraj Hirmanpour , Software Engineering Intitute
Gargi Keeni , Tata Consultancy Services
This article introduces a special section on "Embedding Statistical Methods into Software Engineering Practices." It provides a background on Quantitative Process Management and makes the case for why these methods are important. It presents an example of how a model can be developed to predict project outcomes by using data emerging from the performance of process tasks. It discusses how these methods can be used with different software development paradigms. It ends by summarizing develops needed in five different communities in order for these methods to be widely adopted.
Quantitative Process Management, Quantitative Project Management, Quantitative Software Models, Predictive Software Models, Statistical Project Management
Bill Curtis, Girish V. Seshagiri, Donald Reifer, Iraj Hirmanpour, Gargi Keeni, "The Case for Quantitative Process Management", IEEE Software, vol.25, no. 3, pp. 24-28, May/June 2008, doi:10.1109/MS.2008.80
1. D.L. Gibson, D.R. Goldenson, and K. Kost, Performance Results of CMMI-Based Process Improvement, tech. report CMU/SEI-2006-TR004, Software Eng. Inst., Carnegie Mellon Univ., 2006.
2. W.E. Deming, The New Economics, 2nd ed., MIT Center for Educational Computing Initiatives, 1994.
3. T.H. Davenport and J.G. Harris, Competing on Analytics, Harvard Business School Press, 2007.
4. W.A. Shewhart, Economic Control of Quality of Manufactured Product, Van Nostrand, 1931.
5. D.J. Wheeler, Understanding Variation, SPC Press, 1993.
6. W.S. Humphrey, Introduction to the Personal Software Process, Addison-Wesley, 1997.
7. W.S. Humphrey,Introduction to the Team Software Process, Addison-Wesley, 2000.
8. A. Cockburn, Agile Software Development, Addison-Wesley, 2002.
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