Prediction of Software Development Modification Effort Enhanced by
a Genetic Algorithm
Gergő Balogh, Ádám Zoltán Végh and
Árpád Beszédes
During the planning, development, and maintenance of
software projects one of the main challenges is to accurately predict
the modifi cation cost of a particular piece of code. Several methods
are traditionally applied for this purpose and many of them are based
on static code investigation. We experimented with a combined use of
product and process attributes (metrics) to improve cost prediction,
and we applied machine learning to this end. The method depends on
several important parameters which can signifi cantly infl uence the
success of the learning model. In the present work, we overview the
usage of search based methods (one genetic algorithm in particular) to
calibrate these parameters. For the fi rst set of experiments four
industrial projects were analysed, and the accuracy of the predictions
was compared to previous results. We found that by calibrating the
parameters using search based methods we could achieve signi cant
improvement in the overall effi ciency of the prediction, from about
50% to 70% (F-measure).
Keywords: software
development, effort estimation, modi fication effort, genetic algorithm.
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