=Paper=
{{Paper
|id=Vol-1829/iStar17_paper_15
|storemode=property
|title=Argumentation-based Methodology for Goal-oriented Requirements Language (GRL)
|pdfUrl=https://ceur-ws.org/Vol-1829/iStar17_paper_15.pdf
|volume=Vol-1829
|authors=Sepideh Ghanavati,Marc van Zee,Floris Bex
|dblpUrl=https://dblp.org/rec/conf/istar/GhanavatiZB17
}}
==Argumentation-based Methodology for Goal-oriented Requirements Language (GRL)==
https://ceur-ws.org/Vol-1829/iStar17_paper_15.pdf
Argumentation-based Methodology for
Goal-oriented Requirements Language (GRL)
Sepideh Ghanavati1 , Marc van Zee2 , Floris Bex3
1
Texas Tech University, Lubbock, TX, USA
2
University of Luxembourg, Luxembourg
3
Utrecht University, The Netherlands
sepideh.ghanavati@ttu.edu, marcvanzee@gmail.com, f.j.bex@uu.nl
Abstract. Goal-oriented Requirements Language (GRL) aims to cap-
ture goals and non-functional requirements of stakeholders and analyzing
alternative solutions for realizing these goals. GRL also documents the
rationale behind selecting certain goals or alternatives. However, it does
not have any means to document and trace back all of the arguments
that occur during the stakeholder’s discussion process. To address this,
we have developed the RationalGRL framework. RationalGRL combines
techniques for formal argumentation from artificial intelligence with goal
modeling in GRL. However, we did not specify how practitioners can ac-
tually use this framework. In this paper we discuss the methodology for
RationalGRL, which consists of two processes, goal modeling and argu-
mentation, that can be done interchangeably. We motivate our approach
with an example.
Keywords: Goal-oriented Requirements Language, Argumen-
tation Framework, Practical Reasoning
1 Introduction
Goal-oriented Requirements Language (GRL) [1] is part of User Requirements
Notation (URN), an ITU-T standard [4]. GRL models high-level goals and objec-
tives in terms of softgoal or goal which are refined into operationalized solutions
called tasks. GRL includes beliefs which aims at capturing reasons behind select-
ing certain goals or tasks. GRL beliefs are very limited and static. It is also not
possible to reason about them.
In recent years much work has been done to integrate argumentation-based
techniques with goal modeling approaches. Goal Argumentation Method (GAM)
[5] proposed by Jureta et al. applies argumentation and reasoning to decide be-
tween alternatives and goals for developing goal models. GAM includes a mech-
anism to translate formal argument models to goal models. Haley et al. [3] intro-
duce a framework which exploits a set of structured arguments to capture the
rationales behind choosing a set of security requirements. Their work does not
include formal semantics for analyzing and evaluating whether the arguments
are acceptable or not. Murukannaiah et al. [6] also propose Arg-ACH which in-
cludes argumentation techniques to identify and resolve the inconsistencies and
mismatches between the stakeholders’ goals and beliefs.
In recent work, we have proposed a preliminary framework called Rational-
GRL [11, 10, 13] with tool-support [12], which maps argument diagrams with
GRL and helps developing GRL models by allowing for better representation
of the stakeholders’ arguments and discussions. The RationalGRL framework is
similar to GAM in that it also provides a translation between formal arguments
and GRL models. However, the main difference between GAM and our Ratio-
nalGRL framework is that in RationalGRL, the arguments are integrated with
goal models through argument schemes which are used in artificial intelligence
research and philosophy to model discussion about arguments.
While the RationalGRL framework contained various algorithms for com-
puting GRL models from arguments, we did not clarify how practitioners can
actually use this framework. Therefore, in this paper, we discuss the methodol-
ogy for developing GRL models based on underlying discussions and arguments
using the RationalGRL framework.
The rest of the paper is as followed: In Section 2, we discuss practical rea-
soning and GRL. We provide an overview of the RationalGRL methodology in
Section 3. We give a brief example in Section 4 and finally, we conclude and
discuss the future work in Section 5.
2 Practical Argumentation and GRL
Practical reasoning which has been studied extensively in both philosophy [14]
and AI [2], aims at analyzing and reasoning about goals and actions. Walton et
al. [14] introduce an approach to practical reasoning based on arguments schemes
(AS) and critical questions (CQs). In this approach, an instantiation of an AS
can result positively or negatively in taking an action. CQs can be used to test
the AS (for example, to verify whether the action is possible given the current
situation). If the answer to the CQ is negative, a counterargument to the original
argument is instantiated. Atkinson et al. [2] developed the practical reasoning
argument scheme (PRAS) for reasoning about goals and actions. PRAS follows
the basic argument structure as:
We have goal G; Doing action A realize goal G; Which promote value V ;
Therefore, We should perform action A.
PRAS also includes a set of CQs which criticize practical arguments for
acceptance or rejection. Some examples of CQs are: Will the action satisfies the
desired goal? or Are there alternative ways of realizing the same goal?
We apply PRAS to goal modeling to document the stakeholders’ discussions
as formal argumentation. This allows us to compute whether GRL intentional
elements (IEs) and their relationships are shown in the resulting goal model,
or whether they are disabled. This framework includes both AS and CQs. The
framework provides a rationalization to GRL IE in terms of underlying argu-
ments, and it allows one to understand why certain elements have been rejected.
In the next section, we will discuss the integration of the argumentation
framework with GRL which leads to RationalGRL framework as well as the
methodology for developing models in this framework.
3 RationalGRL Framework and Methodology
As mentioned earlier, the RationalGRL framework aims at integrating GRL
models with the underlying discussions and arguments of the stakeholders during
the analysis phase by combining PRAS with GRL. Our argumentation frame-
work is developed based on the analysis of the CQs, practical reasoning and
discussions of the stakeholders of the system. Initial GRL models are first cre-
ated based on the analysis of non-functional requirements in the requirements
specification documents, discussions with stakeholders and by refining the high-
level goals into operationalized tasks.
In RationalGRL, both GRL and argumentation models are done iteratively.
Argumentation models impact GRL models and result in refinement and changes
in GRL. For example, an answer to the CQ Is the task possible? can lead to rejec-
tion or acceptance of the task in GRL. The analysis of GRL models may result
in changes in the underlying argumentation framework as well. For example,
adding a new IE to the GRL model can result in posing new CQs with respect
to the IE and its relationships. Figure 1 shows RationalGRL and the relation-
ships between the different parts. On the right-hand side of the framework, we
have GRL models and on the left-hand side we have argumentation framework.
The links between the two sides illustrates the impacts each side has on the other
side and the changes that occur due to the impacts and the refinements.
Fig. 1: The RationalGRL Framework
To develop RationalGRL, we follow the methodology shown in Figure 2. We
already have an initial GRL model. The steps of the methodology are as follows:
Instantiate Argument Schemes (AS) – In this step, we instantiate one
of the AS from the argumentation framework. PRAS identifies 8 arguments
schemes which we use in our analysis. An example of an argument scheme is
”Goal G contributes to softgoal S ”. An instantiation of an argument scheme
thus corresponds to an argument for or against part of a goal model.
Answer Critical Questions (CQs) – Instantiated arguments can be at-
tacked by counter-arguments. CQs are ways in which AS can be attacked. Each
argument scheme in PRAS includes one or more CQs. For example, for the ar-
gument scheme mentioned above, we have two CQs as: Does the goal contributes
to the softgoal? and Does the goal contributes to some other softgoals?. Note
that, the answer to CQ can also result into “conflict” situation which we do not
Fig. 2: The RationalGRL Methodology
consider here. Answering a CQ may result in an instantiation of a new AS. Thus,
it is possible to go back and forth between this step and the previous one.
Decide on IE and the Relationships – The answers to the CQs can result
in one of the four cases which impact the arguments and corresponding GRL
IE: INTRO, DISABLE, REPLACE or ATTACK. INTRO means that the argument
scheme of the CQ does not get attacked and instead it creates a new argument.
DISABLE means that the IE or the relationship related to the AS needs to be
disabled or removed from the models. REPLACE introduces a new argument and
attack the original argument at the same time. It basically replace the original
element of the AS with a new one. ATTACK is a generic counterargument which
attacks any argument with another argument when a new evidence occur.
Modify GRL Models – Based on the result of step three, the GRL models
can be modified. In this case, either a new IE or a new relationships is introduced,
an existing IE or relationship gets disabled (removed) from the model or finally
an existing IE or relationships is replaced by a new one. This results in a new
modified GRL. The new GRL model can then impact the argument schemes and
instantiate another argument scheme.
In the next section, we provide a short example to illustrate the relationship
between argumentation framework and goal models.
4 Example
Our examples comes from a transcript containing discussions about the devel-
opment of an information system, and they are created as part of two master
theses on improving design reasoning [9, 8].
A group of stakeholders is developing a goal model for a traffic simulator
example and they are modeling the goal simulate of the system using the Ratio-
nalGRL methodology. This proceeds in the following way:
– First they start at step Modify GRL models (Figure 2), and add the IE
Simulate to GRL model (Figure 3, GRL model, top IE).
– Next, they switch to the argumentation part (step Instantiate arguments
schemes). They answer CQ Does Simulate AND-decompose into any tasks?
with Yes: namely tasks “Dynamic simulation” and “Static simulation.
– As a result, two argument schemes are created, namely:
• Actor System has task Dynamic simulation
• Actor System has task Static simulation
– In the GRL model, this corresponds to the addition of two tasks, and an
AND-decomposition from the goal Simulate to these two tasks.
– Next, the stakeholders test the validity of their goal model by answering
more CQ. They answer two CQs:
• CQ Is task “Dynamic simulation” relevant is answered with “No, it is not
relevant since the problem specification explicitly states dynamic simu-
lations are not required”. Thus, the corresponding GRL IE is disabled.
• The decomposition is changed from AND to OR, since it turned out not
both tasks can be implemented together.
The resulting RationalGRL model is shown in Figure 3.
(CQ3) Task Dynamic simulation is not
relevant
CQ3
(AS2) Actor System has task Dynamic
simulation
(AS2) Actor System has task Static
simulation
(AS5) Goal Simulate AND-
decomposes into Static simulation
and Dynamic simulation
CQ10b
(AS5) Goal Simulate OR-decomposes
into Static simulation and Dynamic
simulation
Fig. 3: Argument schemes and critical questions (left), GRL model (right), and
traceability link (dotted line) of the example.
5 Conclusions
In this paper, we give an overview of our RationalGRL methodology, which in-
tegrates argumentation techniques from AI with goal modeling GRL, in order to
incorporate and document the discussions between stakeholders. We also present
an example to show how the integration and analysis work.
In future, we aim at extending the argumentation framework by performing
an empirical studies so that we can capture more AS that are relevant to GRL.
Although PRAS arguments schemes are a good start to analyze and modify GRL
models, there are important differences between PRAS and GRL. For example,
PRAS does not have the GRL notion of “resource”. Thus, not all of the rela-
tionships and GRL IE are covered by the current AS. In addition, the current
process for integrating argumentation framework with GRL is done manually.
In future, we would like to develop a tool-support that can also be integrated
with GRL tool-support, jUCMNav [7], to help analyzing the AS and CQs.
Acknowledgments: Marc van Zee is funded by the National Research Fund
(FNR), Luxembourg, by the Rational Architecture project.
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