Tools for analysis of various static software complexities for mat lab code

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Neha Bharani, Dr. Abhay Kothari


Software code quality, operation, and maintenance are all supported by software metrics. Program metrics such as size, control flow, and data flow metrics all assess different aspects of software complexity. Continual calculation, review, and control are required for these software complexities. Recently, a lot of attention has been dedicated to this difficult issue, because of the commercial value of software projects. In the literature, there are some software metrics and estimation models to measure the complexity of mat lab projects. However, In order to acquire correct findings about software complexity, we must integrate advanced software metrics to the process. This paper reviews the theory of various software complexity metrics and establishes GUI based mat lab tool that calculates a set of complexity metrics such as line of code (LOC), NPATH (NC), McCabb's metrics (MCC), Halstead's Software Science Complexity (HSSC) and Relative System Software Complexity (RSYSC) for mat lab programs to include a wide range of complexity. By identifying new and redefining current measures, we evaluate many software metrics such as software quality, project size/effort, and many more areas. Further, these metrics can be used as inputs in neural networks for more accurate the estimation of software complexity metrics.

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