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Table 1-1 Estimation Approaches and Techniques
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Strengths Based on a depth of historical data Ideal for an indicative estimate early in the life of a project Weaknesses Too imprecise for accurate estimation You need to be confident that the equation being used is relevant to your project The equation always provides an estimate, even if your project is unusual or exceptional Not very useful for small project estimation4 Based on representative past experience that may no longer be relevant For best results, the technique needs to be aligned to your environment/organization Cannot be used when no past experience is available Based on a past experience that may no longer be relevant Difficult to find suitable analog projects For best results, needs to be closely aligned to your environment or organization Subjective, can be optimistic Requires detailed knowledge of the proposed project s structure and individual components Requires extensive knowledge of the organization and development environment May overlook items or activities
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Technique Equation Use
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Create UCC - 12 In None
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Comparison
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Based on representative experience Objective, repeatable, verifiable, defensible Efficient and if used correctly, provides a good guide to the likely effort your project will consume Based on representative experience Objective, repeatable, verifiable, defensible
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Analogy
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UPC Symbol Scanner In C#.NET
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Work Breakdown
Create UPC A In Objective-C
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UPCA Maker In Visual Studio .NET
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Detailed and specific to this project
ECC200 Drawer In None
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Read Bar Code In .NET Framework
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Table 1-2 Estimation Techniques Strengths and Weaknesses
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Although there is no specified size as to what constitutes a small project, for a project measured in function points, most software metrics consultants agree on a lower limit of around 30 function points
1:
project estimation
Other Techniques
Techniques from artificial intelligence research have also been applied to develop software effort estimation models For example, artificial neural networks and decision trees have been used to estimate effort These methods do not require the user to propose an explicit functional form for the model, only the input and output metrics These techniques are beyond the scope of this book
Estimate Ranges
Remember that the earlier an estimate (or if there is little known data, a guesstimate ) is performed, the less accurate it will be For this reason, when relaying an estimate to your customer, you should always provide a plus/minus range to accompany the estimate to indicate the degree of confidence in the estimate Your original estimate is the most likely estimate, while upper and lower figures are generally the optimistic and pessimistic estimates The Project Management Institute s Project Management Body of Knowledge (PMBOK version 4) provides useful guidance on estimate ranges A number of equation approaches can be used to present a weighted average of the estimate (examples include PERT, CPM, Monte Carlo) The following example uses the Program Evaluation and Review Technique (PERT) approach to estimate likely effort for individual project activities: Te = To + 4 Tm + Tp 6
where Te = expected effort To = most optimistic estimate Tm = most likely estimate Tp = most pessimistic estimate
Rather than giving the customer a fixed, single number of effort hours, it is far more helpful to state: Our estimate is 250 hours, plus or minus 50 hours, based on what we know about the project at this stage
Timing of Estimates
Figure 1-2 shows you the impact that your increasing knowledge of the system requirements will have on the accuracy of your estimates Figure 1-35 is provided to assist you in deciding when particular estimating methods are most appropriate in the life cycle of your project
Figure 1-3 is supplied by Charles Symons of Software Measurement Services Ltd
practical Software project estimation
Approx Functional Sizing Detailed Functional Sizing Estimating Uncertainty Design Requirements Feasibility Build + Traditional Task-Based Estimating
Test Implement
Figure 1-2
The cone of uncertainty
Indicative uncertainty (+/ ) in whole 25% project effort estimate
2 1 3
Feasibility Requirements
Design
Build & Unit Test
System Test
Implement
Earliest reasonable application of an FS-based estimating method (in approximate mode)
First fully informed use of task-by-task estimating First fully informed use of an FS-based estimating method
Whole-of-Project Estimating Methods Top-Down 1 Typical FS estimating method (when well calibrated) 2 Estimating by analogy Bottom-Up 3 Task-by-task estimating
Figure 1-3
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