Various inheritance-based Object-Oriented (OO) metrics (high-level) have been extensively used to measure the performance and viability of programs written in OO languages like C++ and Java. They have helped in analyzing programs from a high level perspective. Although Low-Level (LL) metrics (like processor execution time and memory used) have also been used in analyzing both, the OO and non-OO programs, a correlation between OO (high-level) and LL metrics has not been made in detail. Hence, this paper explains how changes made in Object-Oriented Programs (OOPs) according to inheritance-based OO metrics affect an analysis of the same programs that use LL metrics in C++. Such a correlation can be utilized in designing programs effectively so that inheritance is used in a manner in which the least possible OS and hardware-related resources, like processor and memory, are used. Moreover, these correlations will also help a quality engineer choose the correct set of inheritance-based OO metrics for the quality analysis of an OO system, considering the ramifications of LL metrics on inheritance-based OO metrics.

Object-Oriented analysis and design focus on objects as the primary agents involved in a computation with each class of data and related operations being collected into a single system entity. The behavior of the system is achieved through collaboration between these objects, and the state of the system is the combined state of all the objects in it.The focus on process improvement has increased the demand for software metrics to manage the process. A variety of Object-Oriented (OO) metrics (Chidamber and Kemerer, 1991 and 1994; and Lorenz and Kidd, 1994) have been studied and used to analyze the performance of Object-Oriented programs and systems. These OO metrics have helped in analyzing programs from a high-level perspective.