@INPROCEEDINGS { Rsds2740,
author = {Ramanna, Sheela}, 
title = {Rough Neural Network for Software Change Prediction}, 
booktitle = {Rough Sets and Current Trends in Computing}, 
conference = {Rough Sets and Current Trends in Computing}, 
series =  = {Lecture Notes in Arificial Intelligence}, 
volume = 2475, 
pages = {602--609}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {October}, 
year = 2002, 
editor = {Alpigini, James J. and Peters, James F. and Skowron, Andrzej and Zhong, Ning Liu Jiming}, 
issn = {0302-9743}, 
isbn = {3-540-44274-X}, 
abstract = {This paper focuses on calibrating a rough neural network based on software complexity measurements and the corresponding number of changes required to bring a software product (either during development or during post-deployment) into compliance with project standards. A good predictive model for software maintenance that can estimate the number of changes that will allow the early identification of modules that are most likely to require extensive modifications. The results reported in this paper are limited to assessing prediction accuracy based on software engineering data obtained during product development. The Rough Set Exploration System (RSES) is used to derive training and testing sets that are used both by RSES and by a rough neural network toolset named MBnet to predict the number of software module changes needed to bring a module intro compliance with project standards. A comparison between MBnet and RSES in predicting the number of changes for a particular software module is also given.}, 
keywords = {neural network, preprocessing, rough sets, software change prediction, software quality, }, 
}