Software Protection Using Association Rule Mining Akula Sriramulu College Akula Sriramulu College Akula Sriramulu College of engg , Tanuku, AP, India of engg, Tanuku, AP, India of engg, Tanuku, AP,India Abstract Attribute selection is an important activity in data preprocessing for software quality modeling and other data mining problems. The software quality models have been used to improve the fault detection process. Finding faulty components in a software system during early stages of software development process can lead to a more reliable final product and can reduce development and maintenance costs. Ithas been shown in some studies that prediction accuracy of the models improves when irrelevant and redundant features are removed from the original data set. In this study, we investigated four filter attribute selection techniques, Automatic Hybrid Search (AHS), Rough Sets (RS), Kolmogorov-Smirnov (KS) and Probabilistic Search (PS) and conducted the experiments by using them on a very large telecommunications software system. In order to evaluate their classification performance on the smaller subsets of attributes selected using different approaches, we built several classification models using five different classifiers. The empirical results demonstrated that by applying an attribution selection approach we can build classification models with accuracy comparable to that built with a complete set of attributes. Keywords: software quality, probabilistic search, attributes selection approach, and classification models. N.Ravi SVD. Venugopal M.Sateesh Kumar M.Tech (CSE) student Assistant Professor H.O.D of CSE International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 7, September - 2012 ISSN: 2278-0181 1 www.ijert.org