Treffer: Effective analysis and classification of UML class diagrams: Literature review.

Software quality is a crucial component in the creation of software and it remains important to sustain a high design quality in order to embrace new technology and forge to the needs of the users. Of all the quality factors, maintainability is central to the development, while assessing quality during the early design stage can lead to a dramatic enhancement of the final software product. To satisfy these expectations new methodologies and tools demand objective metrics to quantitatively assess and advance software quality as it is developed. This paper deals with the identification and categorization of UML Class Diagrams which are significant in object oriented computer software development and which are the first to be initiated in the development and implementation phases. As class diagrams are primary structural models and define the way as related to the final product, the high quality of class diagrams is a crucial factor. In the present paper, the authors conducted a literature review on different methods and tools for classifying the proposed class diagrams based on metrics that measure cohesion, coupling, and other interrelated factors. As such, through scientific analysis of the achieved literature review findings needed improvements and potential areas for further research are to be identified. Furthermore, this paper discusses the use of machine learning techniques to make automatic classification of class diagrams and determine relevant relationships with other components which in turn improves software design practices. In this paper, I aim at helping early-stage software engineers and developers to enhance the quality of the class diagrams, make the analysis process easier and offer efficient working methodologies that will enhance the overall structure of the diagrams with minimal faults. [ABSTRACT FROM AUTHOR]

Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)