=Paper=
{{Paper
|id=Vol-2490/paper1
|storemode=property
|title=Using GORO to Provide Ontological Interpretations of iStar Constructs
|pdfUrl=https://ceur-ws.org/Vol-2490/paper1.pdf
|volume=Vol-2490
|authors=César Bernabé,Pedro Negri,Vitor E. Silva Souza,Renata Guizzardi,Carla Silva
|dblpUrl=https://dblp.org/rec/conf/istar/BernabeNSGS19
}}
==Using GORO to Provide Ontological Interpretations of iStar Constructs==
https://ceur-ws.org/Vol-2490/paper1.pdf
Using GORO to provide ontological
interpretations of iStar constructs?
César Henrique Bernabé1 , Pedro Pignaton Negri1 , Vítor E. Silva Souza1 ,
Renata S. S. Guizzardi1 , and Carla Silva2
1
Ontology and Conceptual Modeling Research Group (NEMO)
Department of Computer Science, Federal University of Espírito Santo (UFES), Brazil
{chbernabe,vitorsouza,rguizzardi}@inf.ufes.br, pedropn@gmail.com
2
Centro de Informática, Universidade Federal de Pernambuco (UFPE), Brazil,
ctlls@cin.ufpe.br
Abstract. i* is the most popular goal modeling language and, there-
fore, has several dialects that can interpret its concepts in different ways.
iStar 2.0 was designed to lessen the misinterpretation problems of its
constructs. Concepts can be well-defined in the language but, if not used
properly, the produced models may become inconsistent. An ontology
can be used to verify and help the construction of correct and consis-
tent models. GORO is an ontology about GORE that was built based
on different goal modeling languages. This paper interprets some iStar ’s
constructs and proposes some discussions in the light of GORO.
Keywords: i* · iStar · GORE · Ontology
1 Introduction
Due to the popularity of iStar , different extensions have been proposed, leading
to different interpretation of its constructs. This led the community to propose
iStar 2.0 [2] in order to unify and simplify the language [2]. However, some sim-
plifications may cause exactly the same misinterpretation problem as before. Let
us take the example of the refinement link, created to replace the means-end
and decomposition links. This new link allows different interpretations given
that it connects different pairs of elements and, for each pair, the relationship
entails distinct semantics.
Hence, when analyzing models produced using iStar , it is possible to iden-
tify inconsistencies that are not related to misconstructions of the language, but
to improper use of its constructs, due to misunderstandings regarding its con-
structs’ semantics. Moreover, we argue that a syntactic analysis of the language
is not enough. We need a deeper understanding of the constructs with a well
?
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior - Brasil (CAPES) - Finance Code 001. NEMO(.inf.ufes.br) is cur-
rently supported by CNPq (processes 407235/2017-5 and 433844/2018-3), CAPES
(process 23038.028816/2016-41), and FAPES (process 69382549/2015).
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
2 C. Bernabé et al.
founded artifact that provides a common and unique interpretation of concepts:
an ontology.
Several interpretations are given to the iStar constructs, which lead to models
with multiple meanings and confusion among beginners. This motivated us to
analyze the definitions of the language elements in the light of a well-founded
ontology.
One of the main focuses of ontologies is to provide a non-ambiguous con-
ceptualization of a specific domain. As a consequence, modeling languages that
are created with support of an ontology will have a very precise definition of
its constructs. Indeed, giving an ontological interpretation of modeling language
constructs has been recognized as a good practice in modeling language devel-
opment [3], and it is also intended in the iStar 2.0 proposal.
In this paper, we propose the use of the Goal Oriented Requirements Ontol-
ogy (GORO) [7, 1] as a tool to semantically analyze and interpret the constructs
of the iStar language. We use GORO as a reference model and a well founded
basis for (1) making explicit the ontological commitments of the iStar language;
(2) defining (ontological) real-world semantics for their underlying concepts; and
(3) providing guidelines for the correct use of these concepts.
To put it more precisely, in this paper, we provide possible interpretations
of some iStar ’s constructs in the light of GORO. By analyzing different in-
terpretations of constructs, we seek to solidify the definitions of concepts in a
consensual manner. Thus, this paper intends to raise discussions about some
constructs, namely: (i) the Refinement Link; (ii) the Task/Goal refinement inter-
pretation given the different modeling phases to which they belong; and (iii) the
Role element definition and its relation with intentionality.
The remainder of the paper is organized as follows: Section 2 presents a brief
theoretical background on GORO. Section 3 proposes the ontological interpreta-
tion of some iStar ’s constructs based on GORO, also presenting some modeling
guidelines based on such interpretation. Finally, Section 4 concludes this paper.
2 Goal Oriented Requirements Ontology
Due to the large number of languages proposed since the first GORE approach
emerged [6], many concepts became dubiously interpreted and it became in-
creasingly difficult to delimit the scope of the GORE field. Thus, the need for
an artifact that deals with this domain was perceived. In this context, the Goal
Oriented Requirements Ontology (GORO) [7, 1] was proposed. GORO is an on-
tology based on UFO [3] and was built to be used as an interlanguage between
GORE languages and also to be used as an instrument of consensual conceptu-
alization of GORE elements.
The Goal Oriented Requirements Ontology (GORO) [7, 1] was proposed aim-
ing at providing formal semantics to the concepts of GORE and serving as a
common vocabulary for this domain. Moreover, the ontology can be used as ba-
sis for analysis and construction of languages and as a interlanguage between
different GORE approaches. GORO is based on UFO [3] and was built to be
Using GORO to provide ontological interpretations of iStar constructs 3
Fig. 1: The Goal Oriented Requirements Ontology
used as an interlanguage between GORE languages and also to be used as an
instrument of consensual conceptualization of GORE elements.
Figure 1 presents an excerpt of GORO. The ontology focuses on the concept
of Goal and defines it as a Mental Moment, which is a mental property existen-
tially dependent on a single individual. A Goal in GORO can assume four types
of classifications, according to [5]: Functional Hardgoal, Non-functional Hardgoal,
Functional Softgoal and Non-functional Softgoal. A Goal-Based Requirement Arti-
fact (GBRA) (a documented requirement) describes a Goal-Based Requirement
(a requirement that exists only in the stakeholder’s mind). A GBRA can be an
Atomic GBRA or a Complex GBRA. The latter defines a Goal that is decomposed
into smaller ones, either using AND Refinements (AND Complex GBRA), imply-
ing that the supergoal will only be achieved if all of its subgoals are; or in OR
Refinements (OR Complex GBRA), implying that a goal is considered satisfied
when at least one of its subgoals is satisfied.
A Task is a subtype of Action Universal (Plan), which is a specific way to do
something. In this context, a Task describes a process that a stakeholder wants
to follow in order to achieve a Goal. A Task can be an Atomic Task or a Complex
Task. A Complex Task can be broken down into other Tasks, whereas an Atomic
Task cannot. A Task can require and/or produce a Resource.
Assumptions are domain properties expected to be true in a specific con-
text (the context in which a Goal is expected to be achieved). Assumptions are
4 C. Bernabé et al.
classified according to [8]. Contributions are positive/negative total/partial rela-
tionships, which relates a Goal or Task with a Non-functional GBRA.
We emphasize that the entire process of building GORO and mapping con-
structs of GORE languages to GORO (including both versions of i* /iStar) was
carried out with the support of a group of domain specialists, as described in [1].
The iStar constructs definition was extracted from the literature ([2]) and then
mapped to GORO concepts only after the agreement of at least 80% of the
group. The group is made up of five academic professionals with considerable
experience in the field. Table 1 shows the iStar to GORO concepts mapping.
Table 1: iStar to GORO concepts mapping.
GORO Construct iStar Construct GORO Construct iStar Construct
Requirements Stake- Actor, Agent, Role Resource Resource
holder
Functional Require- Goal AND GBRA AND-Refinement
ment Hardgoal
Functional Require- Quality OR GBRA OR-Refinement,
ment Softgoal Qualification
Task Task Contribution Make, Help, Hurt,
Break
3 Ontological Interpretation of iStar Constructs
This section discusses the interpretation of different concepts of iStar and is
divided in three parts. Subsection 3.1 describes the multiple semantics of the
Refinement Link, while subsections 3.2 and 3.3 propose open discussion topics
about the Task/Goal refinement and the Role element, respectively. We use sans
serif and slanted to represent concepts and relations of GORO, respectively, and
boldface for iStar constructs.
3.1 The Refinement Link
With the aim of alleviating the complexity of the language and thus facilitate its
adoption, iStar proposes gathering multiple relations between goals and tasks
in just one relation called refinement link, which can be of two types: AND
or OR. Indeed, this choice simplifies the language’s syntax; however, this can
also lead to confusion regarding semantics. In iStar , a goal can be (AND/OR)
refined in goals and tasks. Also, tasks can be (AND/OR) refined in goals and
tasks. With GORO, we can explore the very nature of all these relations.
The iStar AND refinement between goals is interpreted as GORO’s And
Complex GBRA, which represents a complex Goal (an implicit concept derived
from Goal-Based Requirement, analogous to And Complex GBRA) decomposed in
sub-parts (sub-goals). Being Goal a Proposition, it is possible to separate it in
different parts that, together, compose the original (G ⇐⇒ G1 ∧G2 ∧G3 ∧. . . Gn).
That means, G is satisfied by exactly those situations which satisfy G1 . . . Gn
conjunctively and, so, satisfying all subgoals implies satisfying the supergoal.
Using GORO to provide ontological interpretations of iStar constructs 5
Moreover, the OR refinement element in iStar can be used to represent
GORO’s Or Complex GBRA indicating alternatives, in which Goals are not broken
down in sub-parts but in alternative Goals in which a situation that satisfies each
of them (separately) also satisfies the main goal. Hence, considering S(G) as a
situation that satisfies G: S(G) ⇐⇒ S(G1 ) ∨ S(G2 ) ∨ . . . ∨ S(Gn ).
The iStar refinement link can also be used to refine a goal into tasks.
If the goal is OR refined into tasks, then the child task is a particular way
for achieving the parent goal. In GORO, these relations can be interpreted as
the intends to operationalize relation between a Task and a Goal. This relation
denotes that the successful execution of a Task — i.e., the action of executing
this specific process — will interfere in the reality, producing a post-situation
that may satisfy the Goal. However, if a goal is AND refined into tasks, the
child tasks are sub-tasks that must all be executed to achieve the goal. In this
case, there is an implicit Complex Task that intends to operationalize the Goal
and is OR refined in the child tasks, as explained next.
Regarding task decomposition/refinement, iStar tasks can also be re-
fined in other tasks. When OR refined, the child task represents a specific way
to execute the parent task, whereas when AND refined, the child task is a set
of steps that need to be performed in order to complete the parent task. In
GORO, Tasks can be Complex Tasks or Atomic Tasks. The former can have sub-
parts that, when executed together, achieve the whole Task. The iStar AND
refinement of tasks is in agreement with GORO’s concept of Task decompo-
sition, whereas the OR refinement of tasks is not covered in GORO. We do not
consider OR refined tasks in GORO as we propose a different approach for this
type of modeling: if a task has alternative ways of being achieved, then it is
actually a Goal and hence, its subtasks are Tasks that intend to operationalize
such Goal. Consequently, a task that is OR refined into other tasks should be
modeled as a goal that can be achieved by performing different tasks.
In iStar , it is also possible to refine tasks into goals. This specific type of
refinement will be discussed in Subsection 3.2.
iStar refinements have different meanings depending on the elements they
connect. Ontologically speaking, grouping different semantics into one syntax
leads to construct overload, which may cause these elements to be misinterpreted
by modelers. Nevertheless, we here try to minimize the impact of this issue, by
providing explicit ontological interpretation (i.e., ontological commitment) of the
languages’ constructs. We firmly believe that such interpretation helps clarifying
the meaning of its construct, and ultimately lead to better iStar models.
3.2 The Task/Goal Refinement
In iStar , a task can also be refined in goals. GORO does not predict this kind
of relation. As GORO defines, Goals are the propositional content of an intention
to achieve a specific situation (state of affairs) in reality. This means that Goals
refer to desired situations in reality. On the other hand, Tasks describes specific
processes executed with some intention.
6 C. Bernabé et al.
A possible interpretation is to consider that goals that are children of a
task refer to the post-situation brought about in the reality after that task’s
execution. However, it is our belief that being goals and tasks of different
ontological nature, the trade-off of allowing such refinement is not a positive
one, possibly leading to much confusion. After all, how can a process (i.e., a
Task be decomposed into propositions (i.e., Goals)?
Yu [9] argues that the refinement between goals and tasks is a way to cap-
ture the transition between the problem domain (goal) and the solution domain
(task). In addition, according to him, refining a task into a goal would be nat-
ural in the analysis and modeling cycle, which generally iterates between these
two domains. However, by ontologically analyzing these concepts, the relation-
ship between a task and a goal is not a “refinement”. Rather, the aformetioned
possible interpretation proposes that the task analysis may motivate the “emer-
gence” of new goals, possibly better characterized in different models, created
for the different analysis’ cycles.
3.3 The Role Element
iStar propose actors as active and autonomous entities that aim to achieve their
goals and may be further classified as roles or agents. In GORO, an actor
corresponds to the Requirement Stakeholder Kind concept. A role, in its turn, is
interpreted as a Requirement Stakeholder Role. The distinction between a kind
and a role, according to UFO, is that a kind represents essential properties of
objects (they are also termed rigid or static types [3]), whereas a role represents
contingent or accidental properties of objects (termed anti-rigid types [3]).
As stated before, GORO interprets a Goal as the propositional content of an
intention, which is a mental property existentially dependent on a single individ-
ual. Thus, a Goal is related to an individual Requirements Stakeholder. However,
being a type, a role represents expected behavior patterns of several individuals.
To explain that, we resort to the ontological clarification made by Guizzardi et
al. [4]: “(...) a physical role is characterized by social moment types, which de-
scribe the set of general commitments and general claims that a physical agent
playing a particular role has” (p. 559). Thus, we interpret the goals of a role
as commitments to execute Goals of those particular types. By analogy, similar
interpretations may be inferred to role’s tasks and resources. So, expressing
Goals of Roles may not represent the particular goals of individuals that will
assume that role, but general goals that they are expected to achieve.
4 Conclusion and Future Works
Among the various GORE modeling languages, iStar has been one of the most
popular ones, further leveraging the GORE field. Therefore, conceptually stan-
dardizing all concepts common to this field becomes an increasingly important
need. This is the goal of iStar 2.0. This standard can be considerably improved
with the support of ontologies, such as GORO, making explicit the semantics
Using GORO to provide ontological interpretations of iStar constructs 7
behind the language’s constructs. In addition, ontologies can be used to guide
users in adopting best practices in iStar modeling, thus preventing modelers
from creating inconsistent, low-quality models. We emphasize that an ontolog-
ical analysis is not intended to limit or restrict the use of a language, but to
provide a well-founded and unambiguous interpretation of its concepts.
In this paper, we propose an ontological interpretation for iStar role, we ar-
gue that agent is not a necessary concept, and we explain why it is not a good
idea to refine tasks into goals. Moreover, we presented different ontological in-
terpretations of the iStar ’s refinement link. We acknowledge that this element
groups different semantics in order to reduce the complexity of the language.
However, we emphasize the need to find a balance between simplification and
loss of meaning. We understand that this is a hard task and that the line that
defines a good balance between both sides is thin. Therefore, the use of an ontol-
ogy to mitigate the risk of having the language’s construct misunderstood and
misused may be a suitable approach.
We are currently using GORO to analyze a greater number of languages,
also facilitating their interoperability. In addition, GORO can be used to solidify
GORE concepts, so that they can be properly used, thus allowing new adepts
to learn GORE in a simpler and faster way.
References
1. Bernabé, C., Souza, V., Falbo, R., Guizzardi, R., Silva, C.: GORO 2.0: Evolving an
Ontology for Goal-Oriented Requirements Engineering. In: 7th Intl. Ws. on Ontolo-
gies and Conceptual Modelling (Onto.com) (in press) (2019)
2. Dalpiaz, F., Franch, X., Horkoff, J.: iStar 2.0 Language Guide (2016)
3. Guizzardi, G.: Ontological foundations for structural conceptual models (2005)
4. Guizzardi, R., Guizzardi, G.: Ontology-based transformation framework from tropos
to aorml. In: Yu, E., Giorgini, P., Mainden, N., Mylopoulos, J. (eds.) Social Mod-
eling for Requirements Engineering, chap. 16, pp. 547–570. MIT Press, Cambridge,
Massachusetts (2011)
5. Guizzardi, R., Li, F.L., Borgida, A., Guizzardi, G., Horkoff, J., Mylopoulos, J.: An
Ontological Interpretation of Non-Functional Requirements
6. Horkoff, J., Aydemir, F.B., Cardoso, E., Li, T., Mate, A., Paja, E., Salnitri, M.,
Mylopoulos, J., Giorgini, P.: Goal-Oriented Requirements Engineering: A System-
atic Literature Map. In: 2016 IEEE 24th International Requirements Engineering
Conference (RE). pp. 106–115. IEEE (2016)
7. Negri, P., Souza, V., Leal, A., Falbo, R., Guizzardi, G.: Towards an ontology of
goal-oriented requirements. In: CIbSE 2017 - XX Ibero-American Conference on
Software Engineering (2017)
8. Wang, X., Mylopoulos, J., Guizzardi, G., Guarino, N.: How software changes the
world: The role of assumptions. In: 2016 IEEE Tenth International Conference on
Research Challenges in Information Science (RCIS). pp. 1–12. IEEE (jun 2016)
9. Yu, E.S.K.: Modelling strategic relationships for process reengineering. Ph.D. thesis,
PhD thesis, University of Toronto (1996)