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OO programming has become the most popular technology in software development environment as it is been proven that the maintenance of OO software is comparatively lesser than the other programming languages. But still the burden of software maintenance is not completely eradicated. One popular software maintenance approach is the reduction of software maintenance cost by imposing the software evaluation metrics during the development phase of the life cycle. Software metrics helps in identifying the potential problem areas in the code. Many novel metrics have been proposed and only few are validated. The objective of this research is to experimentally explore the two novel OO coupling metrics namely Subclass Coupling Factor (SCF) and Temporal Coupling Factor (TCF) to evaluate their ability to predict the complexity of the built software through statistical validation.

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Software complexity is measured by various metrics of software. Software metrics play an important role in analyzing and improving software quality. Some of the metrics like reliability, reusability, size, etc are proposed by early researchers and they were very useful in software quality measurement. Software measurement faces a number of challenges whose solution requires both innovative techniques and borrowings from other disciplines. Over the years, a number of techniques and measures have been proposed and accessed via theoretical and empirical analyses. This shows the theoretical and practical interest of the software measurement field, which is constantly evolving to provide new, better techniques to support existing and more recent software engineering development methods. Software metrics are often categorized into products and process metrics. The aim of this paper is to discuss and analyse the various metrics which is used for software quality measurement proposed by early authors in this field. And also find a new metrics to improvise the Network Oriented Software Quality Measurement process.