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This paper presents a practical knowledge discovery approach to software quality and resource allocation that incorporated recent advances in rough set theory, parameterized approximation spaces and rough neural computing. In addition, this research utilizes the results of recent studies of software quality measurement and prediction. A software quality measure quantifies the extent, to which some specific attribute is present in a system. Such measurements are considered in the context of rough sets. This research provides a framework for making resource allocation decisions based on evaluation of various measurements of the complexity of software. Knowledge about software quality is gained when preprocessing during which, software measurements are analyzed using discretization techniques, genetic algorithms in deriving reducts, and in the derivation of training and testing sets, especially in the context of the rough sets exploration system (RSES) developed by the logic group at the Institute of Mathematics at Warsaw University. Experiments show that both RSES and rough neural network models are effective in classifying software modules.