STUDY OF EVALUATION OF COMPLEXITY METRICS

Abstract

Software complexity metrics are essential tools for assessing the quality, maintainability, reliability, and performance of software systems. They provide quantitative measures that help developers, testers, and project managers evaluate the structural and functional characteristics of software throughout its development lifecycle. This study examines the concept, significance, and evaluation of software complexity metrics, with particular emphasis on widely used measures such as Cyclomatic Complexity, Halstead Metrics, Lines of Code (LOC), and Object-Oriented metrics including the Chidamber and Kemerer (CK) metric suite. The paper analyzes the advantages, limitations, and practical applications of these metrics in identifying complex code, predicting software defects, estimating maintenance effort, and improving software quality. A comparative evaluation highlights the effectiveness of different metrics in various software development environments and demonstrates their role in supporting informed decision-making during software design, testing, and maintenance. The study concludes that no single complexity metric can comprehensively measure software complexity; instead, a combination of complementary metrics provides a more accurate and reliable assessment, leading to the development of robust, efficient, and maintainable software systems