An Automated Approach for Classifying Reverse-Engineered and Forward-Engineered UML Class Diagrams

Osman, Mohd Hafeez; Ho-Quang, Truong; Chaudron, Michel R. V.

doi:10.1109/seaa.2018.00070

Cited by 14 publications

(8 citation statements)

References 147 publications

(319 reference statements)

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“…Despite its effectiveness, this method to image classification consumes 5.84 seconds per image, which can be problematic when using it with big datasets. Mohd Hafeez Osman et al showed that reverse-engineered and forward-engineered UML class diagrams can be classified by using machine learning [26]. Ahmed and Huang also applied machine learning to classify role stereotypes of UML class diagrams in order to quickly get the knowledge about role stereotypes for developers [27].…”

Section: Related Workmentioning

confidence: 99%

Multiclass Classification of Four Types of UML Diagrams from Images Using Deep Learning

Shcherban¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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UML diagrams are a recognized standard modelling language for representing design of software systems. For academic research, large cases containing UML diagrams are needed. One of the challenges in collecting such datasets is automatically determining whether an image is a UML diagram or not and what type of UML diagram an image contains. In this study, we collected UML diagrams from open datasets and manually labeled them into four types of UML diagrams (i.e., class diagrams, activity diagrams, sequence diagrams, and use case diagrams) and non-UML images. We evaluated the performance of five popular neural network architectures using transfer learning on the dataset of 3231 images that contains 700 class diagrams, 454 activity diagrams, 651 use case diagrams, 706 sequence diagrams, and 720 non-UML images, respectively. We also proposed our neural network architecture for multiclass classification of UML diagrams. The experiment results show that our proposed neural network architecture achieved the best performance amongst the algorithms we evaluated with an accuracy of 98.65%, a precision of 96.76%, a recall of 96.48%, and an F1-score of 96.62%. Moreover, among the neural network architectures that we have evaluated, our proposed architecture has the least parameters (around 2.4 millions) and spends the least time per image (0.0135 seconds per image using GPU) for classifying UML diagrams.

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Section: Related Workmentioning

confidence: 99%

Multiclass Classification of Four Types of UML Diagrams from Images Using Deep Learning

Shcherban¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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“…As many empirical studies in Software Engineering are done on open source projects, the automated extraction and analysis of large sets of project data become possible. For researching the effects of UML modeling in open source projects, we need a way to automatically determine the type of UML use in a project [15].…”

Section: Motivationmentioning

confidence: 99%

Automatic Classification of UML Class Diagrams Using Deep Learning Technique: Convolutional Neural Network

et al. 2021

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Unified Modeling Language (UML) includes various types of diagrams that help to study, analyze, document, design, or develop any software efficiently. Therefore, UML diagrams are of great advantage for researchers, software developers, and academicians. Class diagrams are the most widely used UML diagrams for this purpose. Despite its recognition as a standard modeling language for Object-Oriented software, it is difficult to learn. Although there exist repositories that aids the users with the collection of UML diagrams, there is still much more to explore and develop in this domain. The objective of our research was to develop a tool that can automatically classify the images as UML class diagrams and non-UML class diagrams. Earlier research used Machine Learning techniques for classifying class diagrams. Thus, they are required to identify image features and investigate the impact of these features on the UML class diagrams classification problem. We developed a new approach for automatically classifying class diagrams using the approach of Convolutional Neural Network under the domain of Deep Learning. We have applied the code on Convolutional Neural Networks with and without the Regularization technique. Our tool receives JPEG/PNG/GIF/TIFF images as input and predicts whether it is a UML class diagram image or not. There is no need to tag images of class diagrams as UML class diagrams in our dataset.

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“…The task of classifying UML class diagrams between manually created (for forward engineering) and reverse engineered is tackled in [41]. Several classification and features are tried, over a dataset of 999 UML models.…”

Section: Applicationsmentioning

confidence: 99%

ModelSet: a dataset for machine learning in model-driven engineering

López

Izquierdo²,

Cuadrado

2021

Softw Syst Model

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The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io.

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An Automated Approach for Classifying Reverse-Engineered and Forward-Engineered UML Class Diagrams

Cited by 14 publications

References 147 publications

Multiclass Classification of Four Types of UML Diagrams from Images Using Deep Learning

Multiclass Classification of Four Types of UML Diagrams from Images Using Deep Learning

Automatic Classification of UML Class Diagrams Using Deep Learning Technique: Convolutional Neural Network

ModelSet: a dataset for machine learning in model-driven engineering

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