Modeling Non-Functional Requirements (NFRs) is a challenge, mainly from the subjective character of their elicitation. Modeling NFRs as a collaborative process may create a consensus among stakeholders, combining modeling and elicitation in a learning cycle. However, the collaboration presents challenges, such as the different viewpoints, the influence of creativity, the modeling tool, the domain/scope of the target software system, and the fundamental aspects of configuration management. This paper reports our observations on collaboratively modeling an intentional model for the handling of fruits and vegetables in a futuristic kitchen.
Introduction
i* stresses the "why" rather than only the "what" in software production [
Notwithstanding, collaborative modeling initiatives (browser and cloud-based
solutions) are starting to come out, using CM strategies similar to coding environments [
This paper reports on observations gathered during an experience of modeling the
handling of fruits and vegetables in a futuristic kitchen1. This work relies on
requirements information elicited by a vignette based interviews, and a questionnaire. This
information was elicited from prospective stakeholders of a 2045 kitchen [
Imagine a kitchen in 2045 where the fruit and vegetable delivery service is done through a Drone that uses the kitchen window to leave orders. For food to be ready for consumption, they must go through a food belt to follow a cleaning, organization, and cooling process. model-driven elicitation [10], which fills in the model in question (i* model), we modeled as softgoals (Fig. 2), the qualities found in the questionnaire answers. Later, we evolved the model by adding awareness quality by reusing the Awareness catalog [11].
Susana is the name given to a humanoid that will be able to act as a Requirement
Engineer by 2045 [
Previously, a four-member research group worked towards the understanding of what
a humanoid elicitor would need to meet inhabitant’s needs [
Fig. 3 shows the modeling of the elicited goals [
Three researchers (including the two co-authors) performed the collaborative modeling aiming to understand how a Softgoal Interdependency Graph (SIG) for software awareness [11] would be part of Susana's reasoning. We modeled collaboratively and synchronously (in a research meeting), using GitHub as support for Configuration Management.
With the previous elicited information [
Concerning the effort, about 90 person-hours were spent in modeling without awareness, and about 54 person-hours were used to add awareness [1111] (See the gray elements in Fig. 4).
Briefly, the meetings’ dynamic was: 1) Project the latest version of the model using the tool [20]. 2) Free use of creativity. 3) Modeling in the whiteboard while discussing the proper i* notation. 4) What we agreed in was persisted by the tool [21]. 5) Making notes of tool limitations related to usability, as well as limitations of the i* language. This dynamic intertwined modeling with elicitation based on the needs and the Awareness SIG.
On three occasions and outside the meetings, two members of the group performed the verification of the model produced in meetings. Also, before some of the meetings, one of the researchers performed asynchronous work on the tool [21]. 4
During the modeling, we observed the following challenges: the support of a modeling tool, the different viewpoints, creativity, the problem scope, and, through all of them, the basics aspects of CM.
An intentional modeling tool for web browsers suited our needs for ease of access and the portability of a web browser, together with the freedom provided by a cloud server to persist the models. Also, the availability of the source code of a tool [20] allowed us to adapt it to our needs. We also needed to differentiate the characteristics related to software awareness by using other colors of elements, and arrows as proposed in [11]. The result is an adapted tool [21].
We use GitHub CM to track versions of models made by similar initiatives such as
GenMyModel [
Leite [15] believes that requirements must be obtained using different viewpoints. We used the diversity of opinions, which allowed identifying the identification of missing information and conflicts in the requirements. In our experience in the meetings, we learned to understand each modeler point of view, so that the model evolved more where we had more consensuses and shared understanding; for instance; in the objectives related to the organization of food and packaging (Fig. 4).
However, a challenge encountered in reaching consensus among modelers is the strong influence of explicit NFRs (Table 1) that cross multiple goals, as seen with the sustainability NFR. The implicit NFRs, such as learnability and usability if used, could have taken our model to another result. Another challenge is the time it takes to reach consensus when the requirements for addressing NFRs are being invented [22] in a model-driven elicitation. In time constraints projects, a challenge would be to deal with situations where all points of view win. It is important to stress that different viewpoints strategy is a validation process in itself [15], which corroborates the Linus law. However, it is a challenge to identify errors in an asynchronous collaborative environment.
Creativity in collaboration is a quality that can be affected due to some concerns.
As we follow an i* model-driven elicitation, we found that in the first meetings, the
expertise of one of the members had a negative influence on the equilibrium of interests
of modelers [23]. The necessary training in the technologies used [24] also restricts
creativity. In this sense, the tool [20] often reduced our reasoning for lack of undo
function. Another technology, such as GitHub CM, which will be central to the evolution
of modeling editors [
Finally, the scope of the problem is a challenge when dealing with NFRs. We have identified the lack of modularization techniques to help model-driven elicitation. With more NFRs in the model, whether due to the reorganization of already modeled structures or the addition of new ones, dependency links grew. With more links, visualization became a problem, which led to problems in model understanding. 5
This work presents some of the challenges in NFRs collaboratively modeling, but other studies may be done on the model evolution as registered in the GitHub CM [18]. Future work should address challenges to improve the tool [20]. On the other hand, we are continuing the modeling of Susana as a means to better understand how awareness driven software may profit from intentional modeling. 10. Leite, J.C.S.P. Elicitation Awareness in Conceptual Modeling: The Role of Transparency.
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