asp.net barcode reader free 2: Factors that Influence productivity in Software

Encode QR in Software 2: Factors that Influence productivity

2: Factors that Influence productivity
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Comparison of Popular Productivity Analysis Methods (continued)
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COCOMO II Ratings: VL/L/N/H/VH/XH The meaning of each choice per factor is explained in Barry Boehm s book4
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VAF Ratings: 0 = Not present, or no influence 1 = Incidental influence 2 = Moderate influence 3 = Average influence 4 = Significant influence 5 = Strong influence throughout The guidelines on how to determine degree of influence are explained in the IFPUG Counting Practices manuals5 Coefficient: 065 135
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FiSMA ND21 Ratings: - - = Circumstances much worse than in average = Worse than in average +/ = Normal situation + = Circumstances better than in average ++ = Much better than in average The meaning of each choice per factor is explained in the FiSMA method definition document6
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practical Software project estimation
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Coefficient: The exact value of each choice per factor shall be calibrated by the user The variance of coefficient depends on the calibration
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Coefficient: 05 25 in practice, but theoretically between 01 and 15 Exact values of each choice per factor vary between 088 and 114, based on experience database
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Comparison of Popular Productivity Analysis Methods
4 5 6
Software Cost Estimation with COCOMO II, Barry Boehm et al (Prentice Hall) ISO/IEC 20926: Information Technology Function Point Counting Practices Manual, ISO/IEC, 2003 Finnish Software Measurement Association, FiSMA ry, FiSMA Specification for ND21 available at: wwwfismafi/in-english/methods
2:
Factors that Influence productivity
factors for three commonly used methods Note that COCOMO II is an effort/duration estimation technique where size along with many other factors is a key input, whereas IFPUG and FiSMA are sizing techniques that provide methods that adjust the counted size, not the likely productivity Some of the methods that analyze project-specific productivity factors cover the impact of code reuse with a single question If the impact of reuse is a key issue for your projects, then you should evaluate which methods best address the inclusion of reuse
Summary
Two project characteristics have the most impact on PDR: programming language and team size Other project characteristics that can have an impact include development platform/environment, development type, application type, and application architecture Having established a PDR for your estimate using the ISBSG data, you should then adjust it to reflect your specific environment
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Software estimates: how accurate are they
sing the data from completed projects1, this chapter will provide you with an idea of how people have gone about estimating their projects and how well they did it Use the findings of this analysis to guide your approach to estimating and the allowances that you make to your estimate for the factors specific to your project Use both macro- and micro-estimating techniques to obtain the most reliable estimate Submitters of project data to the ISBSG provide details of the estimation techniques they use in their projects, as well as both estimated and actual project statistics Values for the four key project attributes are sought: project effort, duration, cost, and size The ISBSG Data Repository now has over 850 projects for which estimation data is available Of those, 691 provide estimated and actual project statistics for one or more of the attributes of effort, cost, duration, and size; 661 provide data about estimation techniques used; and 632 projects provide data about statistics and methods This chapter presents an analysis of those projects It summarizes the estimation techniques used, the accuracy of the estimates, and the relationships between estimates In most respects these projects are typical of the full set of projects in the ISBSG Data Repository So the value of the findings presented here is the same as the value of the ISBSG Data Repository as a whole The ISBSG believes that the repository represents the best part of the software industry This is because projects in the repository are complete (and therefore more successful than many projects) and
Refer to Appendix D for details of the project demographics of the data used for the analysis in this chapter
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