Five themes in evaluating tools

The work by Myers et al [97] contains a review of different tools that have been used in both research and industrial settings for creating user interfaces. The authors identify a set of themes that help in identifying strength and weaknesses and in explaining the reasons behind the success or the failure of different approaches.

In this section, we report the definition of the five themes and we inspect GestIT accordingly.

8.2.2THRESHOLD AND CEILING 173

8.2.1

Parts of the user interface that are

addressed

In [97], Myers et al. stated that the successful tools in the development of UIs had a precise target, and they limited their scope only to the task that was needed.

Our modelling approach is focused on the description of gestures, limiting to their temporal evolution. We do not aim to redefine again the other parts of the UI, such as the layout or the behaviour. Instead, we provide a different approach for describing an aspect of the UI definition that is currently spread among different parts of the code, creating what we call the spaghetti

code problem (see section 7.2).

In addition, the proposed modelling technique allows reusing a gesture definition in different applications, since it is possible to attach the behaviour not only to the whole gesture, but also to its sub-parts (the

granularity problem see section 7.1).

The proposed solution, as demonstrated by the supporting library, can be employed with different UI toolkits and do not enforce the developers to select a specific technology, therefore it does not interfere with others aspects of the development.

8.2.2

Threshold and ceiling

According to [97], the threshold is “how difficult is to learn how to use the system”, while the ceiling is “how much can be done using the system”.

In the ideal tool, the threshold is low, while the ceiling is high. This means that the developer or the designer are able to use the tool with little or no training at all and the tool is able to cover appropriately every type of UI that should be created.

In order to evaluate the threshold we should have data on the time needed for learning how to model gestures with GestIT, starting from scratch. Unfortunately, at the time of writing we have no sufficient data for drawing any conclusion. However, we can point out here that the model is based on two different concepts that are familiar for UI developers.

The first are the device related events (e.g. the one related to the touches or the joint positions) that should be understandable for people who design gestural interaction, since they are commonly used in all toolkits.

The second concept is the description of the evolution of the gesture through the time with a set of temporal operators. Such operators are well

known in other contexts and languages, for instance such the CTT [114] for task modelling or LOTOS [18] for process modelling. Therefore, people who already know the semantics of the different temporal operators may apply such knowledge in a different context. Otherwise, the learning path should not take longer with respect to the aforementioned languages and, since they are widely applied in their respectively areas, it should be reasonable to claim that the temporal operators will not constitute a problem for adopting GestIT.

With respect to the ceiling aspect, we can claim that the proposed modelling technique covers adequately the target interaction. This is supported by the different examples of models that we provided in Chapter 4, which cover a broad set of gestures. In addition, we demonstrated that it is possible to apply the model to different existing gesture recognition platforms and that the approach can be easily extended for new ones.

8.2.3

Path of Least Resistance

This path of least resistance aspect is about how “tools influence the kinds of user interfaces that can be created. Successful tools use this for their advantage, leading implementers towards doing the right things, and away from doing the wrong things” [97].

We are confident that our modelling technique is able to “force” the developers to:

1. Create gesture definitions separated from other UI aspects, such as the layout and the behaviour (see section 7.2)

2. Provide means for inspecting the gesture definition and to define reactions at the desired level of granularity (see section 7.1). Such advantages are provided by the way the developer creates the gesture definition in GestIT, and they require the only effort of adopting the model, without assuming any additional technique or pattern.

8.2.4

Predictability

The predictability aspect is about the fact that “tools which use automatic techniques that are sometimes unpredictable have been poorly received by programmers”.

Although we do provide an automatic support for recognizing gestures modelled through the proposed notation, we can claim that such aspect do not represent an issue for GestIT. Indeed, we provided a precise formal

8.2.5MOVING TARGETS 175

definition of both the terms and the composition operators that are involved in a gesture definition. This helps the developers in understanding and predicting the runtime behaviour of the defined model.

However, we are aware that not all developers may be interested in studying the formal definition of the meta-model. Therefore, it may be useful to provide an high level (but obviously imprecise) description of the compositional operators and support the development with an interactive simulator, that may help the developer in finding out himself the recognizer behaviour against a particular event sequence. Such approach has been proved useful for the same set of composition operators in task modelling [114].

8.2.5

Moving Targets

The moving targets aspect is related the fact that, in order to provide a useful support, designer must have a different understating of the target tasks. However, since the development of UI evolves with at a high speed, once the knowledge about how to support a given task is mature, it is possible that such support is no more needed, since the task has become obsolete.

In our case, the moving targets problems does not apply to the gestural interaction itself, since it exists from at least 30 years now, and we can be positive that it will last for a long time. However, it may be related to the change of the supporting technology for recognizing gestures. Indeed, this field proposes an increasing number of recognition devices, and it may happen that a new one device overtakes the capabilities of the existing ones. Therefore, it may be reasonable to shift the development towards a new device even if the supporting tools for the old one are more mature and stable.

We tried to create a model that can be tailored for supporting new devices, providing a definition of ground term that can already cover devices that employ different technologies for tracking gestures. We considered this aspect from the very beginning in order to create a modelling technique able to last more than the recognition devices.