Truong Ho-Quang

Practices and perceptions of UML use in open source projects

Context: Open Source is getting more and more collaborative with industry. At the same time, modeling is today playing a crucial role in development of, e.g., safety critical software. Goal: However, there is a lack of research about the use of modeling in Open Source. Our goal is to shed some light into the motivation and benefits of the use of modeling and its use within project teams. Method: In this study, we perform a survey among Open Source developers. We focus on projects that use the Unified Modeling Language (UML) as a representative for software modeling. Results: We received 485 answers of contributors of 458 different Open Source projects. Conclusion: Collaboration seems to be the most important motivation for using UML. It benefits new contributors and contributors who do not create models. Teams use UML during communication and planning of joint implementation efforts.

Condensing reverse engineered class diagrams through class name based abstraction

In this paper, we report on a machine learning approach to condensing class diagrams. The goal of the algorithm is to learn to identify what classes are most relevant to include in the diagram, as opposed to full reverse engineering of all classes. This paper focuses on building a classifier that is based on the names of classes in addition to design metrics, and we compare to earlier work that is based on design metrics only. We assess our condensation method by comparing our condensed class diagrams to class diagrams that were made during the original forward design. Our results show that combining text metrics with design metrics leads to modest improvements over using design metrics only. On average, the improvement reaches 5.3%. 7 out of 10 evaluated case studies show improvement ranges from 1% to 22%.

Automatic Classification of UML Class Diagrams from Images

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.

An extensive dataset of UML models in GitHub

The Unified Modeling Language (UML) is widely taught in academia and has good acceptance in industry. However, there is not an ample dataset of UML diagrams publicly available. Our aim is to offer a dataset of UML files, together with meta-data of the software projects where the UML files belong to. Therefore, we have systematically mined over 12 million GitHub projects to find UML files in them. We present a semi-automated approach to collect UML stored in images, .xmi, and .uml files. We offer a dataset with over 93,000 UML diagrams from over 24,000 projects in GitHub.

Model-Based Software Engineering: A Multiple-Case Study on Challenges and Development Efforts

A recurring theme in discussions about the adoption of Model-Based Engineering (MBE) is its effectiveness. This is because there is a lack of empirical assessment of the processes and (tool-)use of MBE in practice. We conducted a multiple-case study by observing 2 two-month MBE projects from which software for a Mars rover were developed. We focused on assessing the distribution of the total software development effort over different development activities. Moreover, we observed and collected challenges reported by the developers during the execution of projects. We found that the majority of the effort is spent on the collaboration and communication activities. Furthermore, our inquiry into challenges showed that tool-related challenges are the most encountered.

Diversity in UML Modeling Explained: Observations, Classifications and Theorizations

Modeling is a common part of modern day software engineering practice. Little evidence exists about how models are used in software development and how they help in producing better software. In this talk we introduce a classification-matrix and a theoretical framework that helps explain the large variety of models and modeling styles found in industrial practice. As part of this explanation, we will explore empirical findings on the uses of UML modeling in practice. We intersperse this paper with some insights about modeling in software development that may be common to some, but certainly not generally accepted throughout the software engineering community.

Dissecting Design Effort and Drawing Effort in UML Modeling

One argument in the discussion about the adoption of UML in industry is the supposedly large effort it takes to do modeling. Our study explores how the creation of UML models can be understood to consist of different cognitive activities: (i) designing: thinking about the design (ideation, key-design decision making), (ii) notation expression: expressing a design in a modeling notation and (iii) layouting: the spatial organization of model elements in a diagram. We explain that these different subactivities relate to different short-term and long-term benefits of modeling. In this study we present two controlled experiments with a total of 100 subjects creating models for a small system. In these experiments we focus on software models as represented through UML class diagram. Our results show that at least 56% of the effort spent on creating a class model is actually due to designing. Notation expression is around 41% of the model creation effort and layouting is in the order of 3%. This finding suggests that a significant part of creating models is devoted to design thinking about the problem.