Abstract
Social choice deals with aggregating the preferences of a number of voters into a collective preference. We will use this idea for software project effort estimation, substituting the voters by project attributes. Therefore, instead of supplying numeric values for various project attributes that are then used in regression or similar methods, a new project only needs to be placed into one ranking per attribute, necessitating only ordinal values. Using the resulting aggregate ranking the new project is again placed between other projects whose actual expended effort can be used to derive an estimation. In this paper we will present this method and extensions using weightings derived from genetic algorithms. We detail a validation based on several well-known data sets and show that estimation accuracy similar to classic methods can be achieved with considerably lower demands on input data.
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