Case Study Of GIPSY and MARF
This is an incremental case study for software engineers focused on improving maintenance predictions in specific systems.
The study analyzed software engineering metrics to predict maintenance effort and error rates by examining two open-source systems, MARF and GIPSY, using tools like JDeodrant, LOGISCOPE, and McCabe to prioritize metrics and identify problematic classes.
Metrics are used mainly to predict software engineering efforts such as maintenance effort, error Prone ness, and error rate. This document emphasis on experimental study based on two open source systems namely MARF and GIPSY. With the help of various research papers we were able to analyze and give priorities to various metrics that are implemented with JDeodrant. LOGISCOPE and McCabe tools are used to identify problematic classes with help of Kiviat graph and average Cyclomatic Complexity that further are implemented with highest priority metric with JDeodrant. To obtain accurate results we collected data using different tools. The analysis of the two systems is done as a conclusion of study using different tools.