A Case Study on Quality Attribute Measurement using MARF and GIPSY

TL;DR

This paper compares MARF and GIPSY frameworks using various software metrics, analyzing their quality attributes, identifying weaknesses, and suggesting improvements through empirical evaluation tools.

Contribution

It provides a comparative analysis of MARF and GIPSY's quality attributes using multiple metrics and tools, offering insights into their strengths and weaknesses.

Findings

Identified the most useful metrics for quality assessment.

Detected weak and vulnerable classes in both frameworks.

Provided recommendations for code quality improvement.

Abstract

This literature focuses on doing a comparative analysis between Modular Audio Recognition Framework (MARF) and the General Intentional Programming System (GIPSY) with the help of different software metrics. At first, we understand the general principles, architecture and working of MARF and GIPSY by looking at their frameworks and running them in the Eclipse environment. Then, we study some of the important metrics including a few state of the art metrics and rank them in terms of their usefulness and their influence on the different quality attributes of a software. The quality attributes are viewed and computed with the help of the Logiscope and McCabe IQ tools. These tools perform a comprehensive analysis on the case studies and generate a quality report at the factor level, criteria level and metrics level. In next step, we identify the worst code at each of these levels, extract the worst code and provide recommendations to improve the quality. We implement and test some of the metrics which are ranked as the most useful metrics with a set of test cases in JDeodorant. Finally, we perform an analysis on both MARF and GIPSY by doing a fuzzy code scan using MARFCAT to find the list of weak and vulnerable classes.