This paper presents the results of a study conducted at the University of Maryland to validate a set of Object-Oriented (OO) design metrics introduced by Chidamber and Kemerer (1994) as predictors of fault-prone classes. The study involved eight medium-sized information management systems developed using a sequential life cycle model, a well-known OO analysis/design method, and the C++ programming language. The OO metrics were assessed based on their ability to identify fault-prone classes during the early phases of the life cycle. The study found that several of the OO metrics, such as Weighted Methods per Class (WMC), Depth of Inheritance Tree (DIT), Response For a Class (RFC), and Coupling Between Object Classes (CBO), were effective predictors of class fault-proneness. These metrics were found to be better predictors than traditional code metrics, which can only be collected later in the software development process. The study also showed that the OO metrics were more accurate in predicting fault-proneness than traditional code metrics. The results suggest that these OO metrics can be useful quality indicators for object-oriented software development. The study also highlights the importance of validating OO metrics in industrial settings and suggests that further research is needed to refine and extend these metrics. The study concludes that the OO metrics are useful for predicting fault-proneness in object-oriented software systems.This paper presents the results of a study conducted at the University of Maryland to validate a set of Object-Oriented (OO) design metrics introduced by Chidamber and Kemerer (1994) as predictors of fault-prone classes. The study involved eight medium-sized information management systems developed using a sequential life cycle model, a well-known OO analysis/design method, and the C++ programming language. The OO metrics were assessed based on their ability to identify fault-prone classes during the early phases of the life cycle. The study found that several of the OO metrics, such as Weighted Methods per Class (WMC), Depth of Inheritance Tree (DIT), Response For a Class (RFC), and Coupling Between Object Classes (CBO), were effective predictors of class fault-proneness. These metrics were found to be better predictors than traditional code metrics, which can only be collected later in the software development process. The study also showed that the OO metrics were more accurate in predicting fault-proneness than traditional code metrics. The results suggest that these OO metrics can be useful quality indicators for object-oriented software development. The study also highlights the importance of validating OO metrics in industrial settings and suggests that further research is needed to refine and extend these metrics. The study concludes that the OO metrics are useful for predicting fault-proneness in object-oriented software systems.