DMARF AND GIPSY High Level Architecture and Requirements Analysis

TL;DR

This paper analyzes open-source frameworks DMARF and GIPSY to assess their quality using metrics, UML models, and refactoring strategies, aiming to improve their performance and maintainability.

Contribution

It provides a detailed quality assessment and refactoring approach for DMARF and GIPSY based on metrics, UML modeling, and code analysis, which is a novel application for these systems.

Findings

Identified key quality metrics for open-source systems.

Developed UML models and analyzed code smells.

Recommended refactoring methods to enhance system quality.

Abstract

In the current scenario, many organizations invest on open-source systems which are becoming popular and result in rapid growth, where in many of them have not met the quality standards which resulted in need for assessing quality. Initially we represent our work by analyzing the two open source case studies which are (1) Distributed Modular Audio Recognition Framework (DMARF) is an open-source framework which consists of Natural Language Processing (NLP) implemented using Java which facilitates extensibility by adding new algorithms, (2) General Intensional Programming System (GIPSY) is a platform designed to support intensional programming languages which are built using intensional logic and their imperative counter-parts for the intensional execution model. During this background study we identified few metrics which are used to assess the quality characteristics of a software product defined by ISO standards. Among the metrics, we identified the number of the java classes and methods using SonarQube. Followed by that, the actors and stakeholders have been categorized and focused on the evolution of fully dressed use cases. Besides, we analyzed the requirements and compiled the conceptual UML domain model diagrams with the responsibilities and relationships based on the functionalities, which leads to the creation of the class diagrams. Later the analysis and interpretation of results has been done using the metric tools to verify results which have been implemented and to identify the code smells accordingly. Finally the implication is towards performing the system level refactoring by applying appropriate refactoring methods to enhance the quality and performance of the open source systems. Besides, the respective test cases have been portrayed to ensure that there is not much behavioral change with the existing architecture.