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.

A class diagram of a software system enhances our ability to understand software design. However, this diagram is often unavailable. Developers usually reconstruct the diagram by reverse engineering it from source code. Unfortunately, the resultant diagram is often very cluttered; making it difficult to learn anything valuable from it. Thus, it would be very beneficial if we are able to condense the reverse-engineered class diagram to contain only the important classes depicting the overall design of a software system. Such diagram would make program understanding much easier. A class can be important, for example, if its removal would break many connections between classes. In our work, we estimate this kind of importance by using design (e.g., number of attributes, number of dependencies, etc.) and network metrics (e.g., betweenness centrality, closeness centrality, etc.). We use these metrics as features and input their values to our optimistic classifier that will predict if a class is important or not. Different from standard classification, our newly proposed optimistic classification technique deals with data scarcity problem by optimistically assigning labels to some of the unlabeled data and use them for training a better statistical model. We have evaluated our approach to condense reverse-engineered diagrams of 9 software systems and compared our approach with the state-of-the-art work of Osman et al. Our experiments show that our approach can achieve an average Area Under the Receiver Operating Characteristic Curve (AUC) score of 0.825, which is a 9.1% improvement compared to the state-of-the-art approach.

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There is a range of techniques available to reverse engineer software designs from source code. However, these approaches generate highly detailed representations. The condensing of reverse engineered representations into more highlevel design information would enhance the understandability of reverse engineered diagrams. This paper describes an automated approach for condensing reverse engineered diagrams into diagrams that look as if they are constructed as forward designed UML models. To this end, we propose a machine learning approach. The training set of this approach consists of a set of forward designed UML class diagrams and reverse engineered class diagrams (for the same system). Based on this training set, the method 'learns' to select the key classes for inclusion in the class diagrams. In this paper, we study a set of nine classification algorithms from the machine learning community and evaluate which algorithms perform best for predicting the key classes in a class diagram.