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\title{Effort Estimation}
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Effort estimation consists in predict how many hours of work and
how many workers are needed to develop a project. The effort
invested in a software project is probably one of the most
important and most analysed variables in recent years in the
process of project management. The determination of the value of
this variable when initiating software projects allows us to plan
adequately any forthcoming activities. As far as estimation and
prediction is concerned there is still a number of unsolved
problems and errors. To obtain good results it is essential to
take into consideration any previous projects. Estimating the
effort with a high grade of reliability is a problem which has not
yet been solved and even the project manager has to deal with it
since the beginning.
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Several methods have been used to analyse data, but the reference
technique has always been the classic regression method.
Therefore, it becomes necessary to use some other techniques that
search in the space of non linear relationship. Some works in the
field have built up models (through equations) according to the
size, which is the factor that affects the cost (effort) of the
project the most [Dol00],[KT85]. The equation that relates size
and effort can be adjusted due to different environmental factors
such as productivity, tools, complexity of the product and other
ones. The equations are usually adjusted by the analyst to fit the
real data.
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From this perspective, different equation patterns have come out
[Dol00],[Hu97]. but none of them has produced enough evidence to
be considered the definitive cost function, in case there is one.
Nevertheless, the characteristic that has to be satisfied by the
estimation equation is: the model should be capable of doing its
best on estimating reliably the majority of the real values.
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It hasn't been possible until now to obtain an equation, set of
equations or patterns of equations that can satisfy this premise,
and therefore there is no reference of comparison parameter. Then
it can be assumed that the equations are not a good tool to obtain
an optimum prediction.
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