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.

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Context: This research is positioned in the field of methods for creating software design and the teaching thereof.Goal: The goal of this research is to study the effects of using a collection of examples for creating a software design.Method: We ran a controlled experiment for evaluating the use of a broad collection of examples for creating software designs by software engineering students. In this study, we focus on software designs as represented through UML class diagrams. The treatment is the use of the collection of examples. These examples are offered via a searchable repository. The outcome variable we study is the quality of the design (as assessed by a group of experts). After this, all students were offered the opportunity to improve their design using the collection of examples. We ran a post-assignment questionnaire to collect qualitative data about the experience of the participants.Results: Considering six quality attributes measured by experts, our results show that: 1) the models of the students who used examples are 18% better than those of who did not use examples. 2) the models of the students who did not use examples for constructing became 19% better after updating their models using examples. We complement our statistical analysis with insights from the postassignment questionnaire. Also, we observed that students are more confident about their design when they use examples.Conclusion: Students deliver better software designs when they use a collection of example software designs.

There exists a large body of work on the specification and detection of anti-patterns in the source code of software systems. However, there are very few studies on the origins of the occurrences of anti-patterns in the source code: do the very design of the systems lead to the occurrences of anti-patterns or are anti-patterns introduced during implementation? Knowing when anti-patterns are introduced could help software designers and developers improve the quality of the source code, for example by eliminating fault-prone anti-patterns early during the design of the systems, even before their implementation. Therefore, we detect occurrences of anti-patterns in design models and in the source code of some systems, trace these occurrences between design and implementation, and study their relation and impact on the source code. First, we analyze both the UML design models and the source code of 10 open-source systems and show that antipatterns exist in design models. We observe that, on average, 37% of the classes in the design models that belong to anti-patterns also exist in the source code and also play roles in the same anti-patterns. Second, we investigate two open-source systems to assess the impact of the anti-patterns in their design models on the source code in terms of changes and faults. We show that classes that have anti-patterns in the design models have more changes and faults in the source code. Our results suggest that the design of the systems lead to anti-patterns and that the antipatterns impact negatively the change-and fault-proneness of the classes in the source code. Thus, designers should be wary of anti-patterns in their design models and could benefit from tools that detect and trace these anti-patterns into the source code.