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Open source software systems are playing important roles in many scientific and business software applications. To ensure acceptable levels of software quality Open source software (OSS) development process uses advanced and effective techniques. Quality improvement involves the detection of potential relationship between defect and open source software metrics. Many companies are investing in open source projects for making effective software. But, because open source software is often developed with a different management style and groups of people than the industrial ones, the quality and reliability of the code needs to be investigated. Hence, more projects need to be measured to obtain information about the characteristics and nature of the source code. This paper presents an empirical study of the fault prediction capabilities of object-oriented metrics given by Chidamber and Kemerer. We have carried out an empirical study and tried to find whether these metrics are significantly associated with faults or not. For this we have extracted source code processed it for metrics and associated it with the bugs. Finally the fault prediction capabilities of object oriented metrics have been evaluated by using Naïve Bayes and J48 machine learning algorithms.