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Estimating software project effort by analogy simply involves finding one or two past projects that have similar significant attributes to the target project that you wish to estimate An estimate of the effort to complete a new software project is made by comparison with one or more previously completed projects Estimating software project effort by analogy usually involves a number of steps: 1 Establishing the attributes of the target project, and then measuring or estimating the values of those project attributes Analysis of the ISBSG data has revealed attributes that have an impact on project delivery rate Table 9-1 is a guide to the attributes that you should consider2 2 Searching a repository of completed projects for a project that closely matches the target project as a source analog to compare against 3 Using the known effort that was used in developing the source analog as an initial estimate for the target project 4 Comparing the chosen attributes (for example, size, platform, and so on) for the target and source projects 5 Establishing or adjusting the initial effort estimate in light of the differences between the target and source projects
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This list of attributes reflects those that the ISBSG data has revealed have a significant impact on project delivery rate If, however, you think that other attributes included in the ISBSG Repository, or in another repository that you are using, are relevant for your own project, then you should also consider those additional attributes in your estimation analysis
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Table 9-1 Attributes That Impact Project Delivery
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It is very important that you use your judgment to exclude inappropriate analogs and not be tempted to adopt a likely analog without due care It is not clear how best to judge the appropriateness of a potential analog for a target project Analogical tools can assist in the selection process by ranking past projects according to how well they match the target Once an analog has been selected, you are faced with the question of how best to use it to derive an estimate for the target project It is probable that the analog differs from the target project in some respects that influence effort You need to think about what adjustments should be made to the effort value of the analog to reflect these differences
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Note It is very important that you use your judgment to exclude
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inappropriate analogs and not be tempted to adopt a likely analog without due care
Advantages of Estimating by Analogy
The accuracy of estimates from experiments with analogy tools demonstrates that software effort estimation by analogy is a viable estimation method Analogy-based estimation also offers some advantages: It is easy to understand the basis for an estimate Analogy-based estimation is quite different from the input-output models, as estimates are based on concrete past examples People are used to seeking out an analogy to help them estimate everyday tasks (We regularly estimate the likely duration of a planned journey based on previous experiences) This familiarity may explain why people are comfortable estimating in this manner It is useful where the domain is difficult to model We know that many factors influence the effort needed to complete a software project We know less about how these factors interact with each other, or how best to model the range of factors via software
practical Software project estimation
metrics Estimation by analogy can be used successfully without having a clear model of how effort is related to other project factors It relies primarily on selecting a past project that is a close match to the target project, rather than assuming a general relationship between effort and other project characteristics that applies to all projects It can be used with partial knowledge of the target project Analogybased estimation allows people to use whatever information they have available to search for and select an analog, rather than prescribing particular inputs It can avoid the inaccuracies of equation-based model use Analogybased estimation has the potential to provide accurate estimates even using another organization s data, provided an appropriate analog for the target project is found within the data set used for estimation An analog is appropriate if effort and associated factors are related in a similar way for both the target project and analog It has the potential to mitigate problems with outliers3 Analogybased estimation does not rely on calibrating a single model to suit all projects If the target project is typical of a data set, it is likely that one or more appropriate analog projects will be found to base the estimate on Outliers in the data set have no influence on the estimate at all If the target project is itself an outlier, at least the lack of a similar project to compare against may make this apparent to the estimator When using an equation-based model, an estimator may be lulled into a false sense of security because the model will generate an estimate even for the outlier
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