Automatic Classification of UML Class Diagrams from Images

Abstract

Graphical modelling of various aspects of software and systems is a common part of software development. UML is the de-facto standard for various types of software models. To be able to research UML, academia needs to have a corpus of UML models. For building such a database, an automated system that has the ability to classify UML class diagram images would be very beneficial, since a large portion of UML class diagrams (UML CDs) is available as images on the Internet. In this study, we propose 23 image-features and investigate the use of these features for the purpose of classifying UML CD images. We analyse the performance of the features and assess their contribution based on their Information Gain Attribute Evaluation scores. We study specificity and sensitivity scores of six classification algorithms on a set of 1300 images. We found that 19 out of 23 introduced features can be considered as influential predictors for classifying UML CD images. Through the six algorithms, the prediction rate achieves nearly 96% correctness for UML-CD and 91% of correctness for non-UML CD.

When testing meets code review

“…To rank the attributes, we used Weka [6], a suite of machine learning software written in Java. Weka provides different algorithms for identifying the most predictive attributes in a dataset-we chose Information Gain Attribute Evaluation (InfoGainAttributeEval), which has been extensively used in previous literature [9,26,40]. In-foGainAttributeEval is a method that evaluates the worth of an attribute by measuring the information gain with respect to the class.…”

Automatic classification of web images as UML diagrams

“…Ho-Quang et al [10], based on a previous work by Hjaltason and Samúelsson [11], and also in collaboration with Karasneh, further investigate image features that can be effectively used to classify images as class diagrams. Using a training set of 1300 images, and with a success rate lower but similar to ours (90%-95%), they need instead a much higher average processing time to classify each image, nearly 6 seconds (our tool requires less than 1 second).…”

Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques

“…The previous two works were continued in Ho-Quang et al [25], who further investigated image features that can be effectively used to classify images as class diagrams and to train an automatic classifier. They were able to reduce the number of image features from 23 to 19, and the processing time from 10 to 5.84 s, using six different learning algorithms with the same training set of 1,300 images.…”