Software modeling is a key activity in software development, especially when following any kind of Model-Driven Software Engineering (MDSE) process. In this context, standard modeling languages, like the Unified Modeling Language (UML), and tools for supporting the modeling activities become essential.

But how are UML class diagrams built in practice? and how well modeling tools support modelers on this task?

In our paper recently published on the Computer Standards & Interfaces Journal (free author copy ), we employ two research approaches to analyze these questions. Our goal is to draw some useful lessons that help to improve the (UML) modeling process both by recommending changes on the tools themselves and on how UML is taught so that theory and practice of UML modeling are better aligned.

Our study is focused on two tools widely used by the community: MagicDraw (version 18.0 Personal Edition, from 2017) and Papyrus (Neon 2.0.X version, from 2017). We have chosen these tools because they are two of the most popular modeling tools and they cover a representative group of modelers. MagicDraw is one of the major commercial modeling tools, recently acquired by Dassault Systems to strengthen its set of System Engineering tools while Papyrus is the reference open-source modeling tool in the Eclipse community.

The study focuses on three research questions (RQ):

  1. RQ1: What modeling strategy do modelers follow to specify UML class diagrams? This RQ is centered on the approach employed to model the class diagram, i.e. the sequence of actions that are executed by the modeler to draw it. According to the focus and the granularity of the analysis, the modeling strategy can be decomposed into two sub-perspectives:
    1. the global modeling strategy, i.e. the general approach followed to create the whole class diagram (we define in the paper two global modeling strategies inspired in two graph theory algorithms: Breadth Modeling and Depth Modeling); and
    2. the specific modeling strategy, i.e. the particular approach followed to create specific parts of the class diagram such as the attributes and associations.
  2. RQ2: How much modeling effort (time and number of clicks) is needed to model UML class diagrams? This RQ focuses on evaluating the degree to which modeling tools support and facilitate the construction of UML class diagrams through its graphical interface. In our study, the modeling effort is evaluated in terms of time and number of clicks needed to model the class diagram. According to the focus of the analysis, the modeling effort can be studied from two points of view:
    1. the effort devoted to initialization tasks, i.e. the work devoted to initialize the modeling tool and create an empty class diagram; and
    2. the total modeling effort, i.e. the complete effort devoted to modeling the whole class diagram, including the effort devoted to initialization tasks.
  3. RQ3: What are the most common obstacles (difficulties and errors) encountered by modelers when modeling UML class diagrams? This last RQ is centered on the difficulties participants encountered with the use of modeling tools as well as the mistakes made by the modelers while using these tools. In both cases, obstacles have been analyzed and classified under the CRUD (Create, Read, Update and Delete) perspective, that is, the obstacles are contextualized according to whether they have arisen when the elements of the diagram are Created (C), Read (R), Updated (U) or Deleted (D).

To answer these research questions, we carried out two studies employing different research approaches. The main one is an empirical experiment supported by more than 12 hours of screen recordings (interaction with the modeling tools including verbal audio comments) registered by undergraduate students during the construction of a UML class diagram. Besides, an analytical analysis based on GOMS (Goals, Operators, Methods and Selection Rules) complements the previous experiment.

Example Class diagram used in the empirical evaluation

Example Class diagram used in the empirical evaluation

The design and the results of both studies are detailed in the paper. Here we only would like to outline the findings of both studies and several recommendations to improve the performance of UML modeling tools as well as the way of how UML modeling is taught in order to overcome the detected challenges.

See some conclusions on the usability of UML tools after analyzing over 10h of video recordings of students trying to draw a UML class diagram Click To Tweet

Modeling Strategy

In terms of the modeling strategy, the most relevant finding from our empirical experiment is that there is no relevant difference in terms of the global modeling strategy (breadth, depth or alternately modeling).

This variable modeling strategy is in part due to the unrestricted approach followed by the modeling tools that let modelers draw diagrams as they please. And while this freedom is, in principle beneficial, it adds a new hurdle to novel modelers that would probably prefer a more systematic approach and guidance to model. As such, we recommend either teachers or (even better) tools to suggest a modeling strategy to novice modelers until they feel empowered enough to adapt the strategy to their own perspective on how to be most efficient at modeling.

A typical strategy to be used could be, for instance, using a top-down view (which directly derives from the breadth modeling strategy proposed in this work), which consists in:

  1. Identify and model the classes (which classes do we need?), then
  2. Identify and model associations (how are the classes connected?), and finally,
  3. Identify attributes and multiplicities (what do we want to know about the objects?). If enforced by the modeling tools, this functionality should be optional and be switched on/off by the modeler at will.

Even though we suggest recommending a specific modeling strategy, the results of our empirical experiment did not find significant differences in terms of effort (time and number of clicks) wrt the used strategy. In particular, the collected data reveals that the students who follow the depth modeling strategy took 9 minutes on average to model the whole class diagram, while the students who follow the breadth modeling strategy took 10 minutes and the students who follow an alternate strategy took 11 minutes. Similarly, regarding the number of clicks, the students who follow the depth modeling strategy took 127 clicks on average to model the whole class diagram, while the students who follow the breadth modeling strategy took 133 clicks and the students who follow an alternate strategy took 119 clicks.

In fact, the ideal modeling strategy, especially when it comes to the modeling of specific individual types of elements like attributes or associations, can also depend on the particular UI of the modeling tool. For instance, in MagicDraw a user can create an attribute directly introducing its name and type inside the class box (and obviate the rest of its properties unless it explicitly wants to indicate them by first opening the extended menu). Instead, in Papyrus each time a user creates an attribute, the extended menu is automatically opened at the bottom of the screen and all its properties are visualized. This could explain why only the users of the second group specified some properties such as the visibility and multiplicity of attributes.

Modeling Effort

From our empirical experiment, we observed that the effort (measured as time and number of clicks) devoted to building a class diagram is significantly different when using MagicDraw and Papyrus. This difference is related to the obstacles the participants found using both tools (slightly more in Papyrus), as we will discuss in RQ3. As a curiosity, 13% of the participants (considering both groups) did not finish the whole class diagram in 15 minutes. This is clearly a trade-off to convince software developers to use modeling tools.

Moreover, from our analytical experiment we conclude that, during the initialization tasks (creating a new project and an empty UML class diagram), MagicDraw is more usable in terms of efficiency (amount of steps and estimated time). For instance, on average, MagicDraw requires 9.5 steps and 6.15 seconds less than Papyrus to create a new project and 14.33 steps and 10.47 seconds less than Papyrus to create an empty class diagram. However, in the rest of the tasks devoted to the construction of the UML class diagram both MagicDraw and Papyrus present similar usability. In particular, MagicDraw is slightly more efficient when adding attributes and creating association classes while Papyrus is more efficient when creating UML classes and binary associations.

When taking into account both experiments, we conclude that globally the effort and efficiency could be improved in both tools. Many times modelers were not able to optimally use the tools and end up being much more inefficient than the optimal solution. For instance, when the tool offers several alternatives to achieve the same goal (e.g. creating an association class), modelers do not always choose the simplest path. A similar trade-off between freedom and efficiency as above. We encourage tools to rethink some of their UI components and be more opinionated with their interface in order to prevent clearly inefficient modeling strategies.

Do you think modeling sucks? I blame the tools Click To Tweet

Modeling Obstacles

An exhaustive list of modeling obstacles modelers encountered during the experiment is detailed in the paper. Many of these obstacles are due to the inexperience of the modelers. For instance, in many of the recorded videos, we observed students having difficulties navigating the tools’ menus and options, forcing them to attempt the same task several times before finding the proper way to perform their goal. As explicitly evident in most of the videos, this generated a considerable feeling of frustration among them.

To overcome the modeling obstacles detected in our study (and indirectly decrease the modeling effort), we suggest several recommendations mainly oriented to improve the usability of modeling tools:

  • First, and most important, modeling tools should fix their current known issues to increase the level of trustiness in them. For instance, in the Neon version of Papyrus, it is not possible to introduce the multiplicity of an association class created using the AssociationClass element in the palette.
  • Modeling tools should include guidance for beginners to perform specific tasks. This guidance should be available under user request, for instance, as short embedded videos showing how to carry out specific tasks using the tool (such as creating association classes, etc.). Modeling wizards could also be created for this purpose.
  • Modeling tools should provide useful tips not only when the tool is opened but when the tool detects the modeler really needs such tip. For instance, tools could detect when the modeler is in a loop trying to achieve a specific task and suggest useful and personalized tips to help her. AI techniques could be employed to detect such tool learning challenges and react accordingly.

Although most of the obstacles observed are directly related with the modeling tools, like the ones above, other obstacles are more linked to a general lack of modeling knowledge. To improve them, we would like to see some tweaks on how we teach modeling:

  • Modeling instructors should explain that some elements in a class diagram should be created following a specific order, even if it is just for pragmatic reasons.
  • Instructors should also emphasize the use of the best practices in conceptual modeling.
  • And they should recommend the use of proper names to facilitate the understandability of the class diagrams, for instance making use of the naming guides.

To sum up, our experiments report that there are no notable differences regarding the usability of MagicDraw and Papyrus. But only because both showed important shortcomings that could be improved to provide a significantly better modeling experience. 

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