By Andrea De Lucia*, Aldo Persico°, Eugenio Pompella°, Silvio Stefanucci* This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. 
 
* Faculty of Engineering - University of Sannio Palazzo Bosco Lucarelli, Piazza Roma - 82100 Benevento, Italy 
 ° EDS Italia Software S.p.A. Viale Edison - Loc. Lo Uttaro - 81100 Caserta, Italy 
 
 
Abstract 
 This paper reports on an empirical study aiming at improving the cost prediction model currently used in a major software enterprise. We used a multiple regression model and the data collected from two corrective maintenance projects. The improvement of the model performances is achieved by taking into account different corrective maintenance task typologies, each affecting the effort in a different way. 
 
 

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