Software Project Effort Estimation with Voting Rules

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.