Rationalization of Goal Models in GRL using
Formal Argumentation
Marc van Zee,∗ Floris Bex,† Sepideh Ghanavati‡ ,§∗ Department of Individual and Collective Reasoning, University of Luxembourg, Luxembourg† Department of Information and Computing Sciences, Utrecht University, The Netherlands‡ Luxembourg Institute of Science and Technology (LIST), Luxembourg§ School of Computer Science, Carnegie Mellon University

Contact: marc.vanzee@uni.lu, sepideh.ghanavati@list.lu, floris.bex@uu.nl

Abstract—We apply an existing formal framework forpractical reasoning with arguments and evidence to theGoal-oriented Requirements Language (GRL), which ispart of the User Requirements Notation (URN). This
formal framework serves as a rationalization for elements in a GRL model: using attack relations between
arguments we can automatically compute the acceptability status of elements in a GRL model, based on
the acceptability status of their underlying arguments
and the evidence. We integrate the formal framework
into the GRL metamodel and we set out a research tofurther develop this framework.
Index Terms—User Requirements Notation, Goaloriented Requirements Language, goal modeling, formal argumentation

I. Introduction

argumentation based on evidence [5]. The formal semantics [7] of arguments and counterarguments underlying theargumentation framework allow us to determine whether
the elements of a GRL model are acceptable given thepotential contradictory evidence and stakeholders’ opinions.
Thus, we add a new formal evaluation technique for goalmodels that allows us to assess the acceptability of elementsof a goal model (as opposed to the satisfiability [1]).
Other authors, most notably Jureta et al. [14], haveapplied formal argumentation methods to goal rationalization. Our framework shares many of the basic ideas of thisother work. However, in section VI we motivate that in our
research agenda we significantly extend this earlier work,
offering a richer, more standardized and fully implemented
framework.
The structure of this paper is as follows: Section II
provides a simple fictitious running example that illustrates
the key elements of our framework. In Section III we present
our main technical contribution: the formal framework forpractical reasoning based on arguments, and in Section IV
we show the starting point of the implementation of thisframework into the GRL metamodel. We set out our
research agenda for future work in Section V, and in
Section VI we discuss related work.

Goal modeling is an essential part of all development
processes, especially in the area of complex reactive anddistributed systems [16]. The Goal-oriented Requirements
Language (GRL) [2] is part of the User Requirements
Notation (URN) standard [13]. GRL aims to capturebusiness or system goals, (sub)goals and tasks that help
achieve high-level goals [1]. GRL allows for several types ofanalysis and evaluation techniques to assess the satisfactionof goals and thus to decide on high-level alternatives.
Although it is currently possible to rationalize GRL
II. Running Example: Best Furniture Inc.
models through so-called belief elements, these beliefs are
Best Furniture Inc. is a fictitious company that builds
single and static statements. Such static elements do not
capture the dynamic goal modeling process, nor do they and sells furniture. They recently noticed a decrease of
capture the discussion process between stakeholders in income from store purchases. To this end, the stakeholders
which high-level softgoals are translated into subgoals, are discussing how to adapt their business processes inwhich are in turn translated into tasks [16]. Hence, the order to make shopping at the store more attractive, andGRL model with the belief elements is merely an end in this way increase the revenue.
A CRM expert argues that improving customer support
product of this process, which does not reflect how themodel was created, i.e., what reasons were used to choose for store purchases will most likely lead to an increase ofcertain elements in the model and to reject the others and profit, simply because customers will be more satisfied.
While thinking of ways in which customer support may bewhat evidence was given as the basis of this reasoning.
In this paper, we develop an initial framework for tracing improved, one of the stakeholders mentions that they have aelements of a GRL model to discussions between stakehold- very weak policy for returning products: It is currently not
ers, captured as arguments and counterarguments based on possible to return products after buying them. A sales clerk
evidence. The framework is based on the formal argumenta- confirms that there have been regular complaints about thistion framework ASPIC+ for structured argumentation [15], policy. Another stakeholder disagrees and points out that
extended with ideas on practical argumentation [3] and Best Furniture Inc. should be careful with such a return

policy, because if too many customers return their products
it may in fact cost the company a lot of money. However,the stakeholder in favor of the return policy is able toproduce data from other furniture companies showing that
customers tend to keep the furniture that they have, andtherefore there will not be many returns.
After this discussion, the stakeholders decide to implement the possibility for customers to return their products.
The stakeholders agree that if a customer would like toreturn a product, then the following three conditions should
be satisfied: First, The product is bought from company“Best Furniture Inc.”. Secondly, the customer has a receipt
for the product, and thirdly, the product is undamaged.
A. The Running Example in GRL

evidence contributes to, for example, the strength of thislink is not captured in the model.
III. A Framework for Goal-based Argumentation
with Evidence
Reasoning about which goals to pursue and actions totake is often referred to as practical reasoning. Practicalreasoning has been studied extensively in formal argumentation, most notably by Atkinson et al. [3], who define thefollowing basic argument structure for reasoning from goals
to actions.
I have goal G
Doing actions A will realize goal G(1)

Therefore I should do actions A.

We assume familiarity with the basics of goal modelingThis basic argument can be further extended to captureand GRL, and we refer to [1] for a more detailed overview. subgoals (i.e., realizing goals G1 , . . . , Gn will allow me to
A GRL model of our example is depicted in Figure 1. The realize goal Gi ).
goal () Implement Return Policy contributes positively
Practical reasoning is defeasible, in that conclusions
to the softgoal () Improve Customer Support, which in which are at one point acceptable can later be rejected
turn contributes positively to the softgoal Increase Profit. because of new information or claims. For example, there
The goal Implement Return Policy is decomposed into three might be unwanted side effects to performing action A,
tasks () Check for receipt, Check if item is bought, and or there may be alternative actions that also lead to theCheck for damage. The rationalization of the choice to realization of one’s goals. Atkinson et al. [3] define a formal
implement a return policy is provided by attaching a belief set of critical questions that point to typical ways in whichelement ( ) to the corresponding GRL element.
a practical argument can be criticized. Our assumption isComparing the GRL model to the description of the that the dynamic discussions about goals and actions in
discussion between the stakeholders shows that although GRL can be captured using practical argumentation.
the GRL model is able to model the goals, subgoals andArguing about goals and actions also contains an elementtasks, it does not capture the stakeholders’ arguments. For of epistemic reasoning, as people might disagree on whether
example, there was an extensive discussion about the goal action A really realizes goal G, or on whether goal G is actuImplement Return Policy: one stakeholder argued that it ally a valid goal, and so on. The earlier example of the CRM
was too expensive, but another stakeholder countered this expert, for instance, is about the (causal) link between
by saying that not many people return furniture.
increasing customer support and the increase of profits.
In GRL, these links are captured by the contribution andcorrelation links and, whilst it is possible to indicate aquantitative value, it is impossible to argue about them,or support them with evidence. In a practical argument,such links are captured by one of the premises - i.e. ‘doing
action A will realize goal G’ or ‘realizing goals G1 , . . . , Gn
will allow me to realize goal Gi ’ - and we can use further
arguments to reason about these premises, supporting or
countering them with evidence (cf. Bex et al. [5]). For
example, we can use the CRM expert’s statements as
support for the link between customer support and profits,
or counter the expert’s statements with an argument basedon data that shows no increase of profits for increased
customer support.
Fig. 1: GRL Model of the running example
In order to rationalize and formally evaluate our goals
and beliefs using argumentation, we choose the ASPIC+
A further shortcoming of the GRL model is that it does
framework for structured argumentation [15]. This framenot capture the evidence given for various elements of thework allows us to define our own inference rules for
GRL model. For example, the CRM expert argued thatpractical and epistemic reasoning inspired by [3] and [5]increasing customer support leads to more profit. This
and, since ASPIC+ is an instantiation of Dung’s [7] abstract
claim is represented by the positive link between Improve
framework, we can use the standard argumentation calculus
Customer Support and Increase Profit, but the way theto compute whether we are justified in accepting certain

goals in our GRL diagram given the criticisms and evidence
that have been brought forward.

The set R of inference rules consists of two different
types of inference rules: deductive or strict inference rules(Rs ) are those of propositional logic and permit deductive
A. The ASPIC+ Framework
inferences from premises to conclusions; and defeasible
In recent decades, the computational study of argu- inference rules (Rd ) represent uncertain inferences thatmentation has received increasing attention, especially can be attacked. Defeasible inference steps are of the form
following the influential paper of Dung [7] on argumentation A |∼ b (from a set of premises A we can defeasibly infer
semantics. The basics of computational argumentation stem b).
from classical logic: given a set of premises (expressed in
We introduce two new defeasible inferences rules fora logical language) and a set of inference rules, we can practical arguments.
infer a conclusion. The ASPIC+ framework captures thesePA
Gq, a contributes to q |∼ Aa
elements in the definition of an argumentation theory, which
consists of a logical language L, a knowledge base K and Here, a is an action and q corresponds to a goal. Thus, if
a set R of inference rules. We discuss each of these three q is a goal and doing action a contributes to q, then weelements in turn.
should perform a.
In our case, the logical language L is propositional logic
PG
Gq, p contributes to q |∼ Gp
with modalities G for goals 1 , B for beliefs, A for actions,and E for evidence, which allows us to express the GRL This inference rule expresses that if q and p are goals, andelements. The language is further extended with a con- realizing p contributes to q, then we should make p a goal.
nective contributes to for expressing the contribution and In addition to these two rules for practical reasoning, we
decomposition links of GRL. For instance, the goal from our can also formalize inferences based on evidence throughrunning example to improve customer support is expressed specific defeasible rules. For example, the statement givenby G(improve cs), while B(few product returns) ex- by the CRM expert (section II) is a reason to believepresses the belief that few people will return products, that the returning a product improves customer supportA(check receipt) expresses the action of checking the is formalized as follows:
receipt, and E(CRM expert) expresses evidence provided
EV1 E(CRM expert) |∼ improve cs contributes toby a CRM expert. Two examples of contributes to conincrease profit
nectives from our running example are: “return_productcontributes to improve_cs” and “ receipt ∧ bought ∧ Similarly, the statement by the company’s sales clerk – thatdamaged contributes to return_product”. The first expres- implementing an option to allow people to return products
sion states that returning a product for money contributes will improve customer service – can be formalized as follows:
to improving customer support. The second expression
EV2
E(sales clerk) |∼ return productshows how decomposition links can be captured using
contributes to improve cs
conjunctions: if the customer has a receipt, the item wasbought and not damaged, then the product can be returned.
The knowledge base K specifies the body of information
B. Building Practical Argumentsfrom which the premises of an argument can be taken. A
G(return_product)
distinction is made between ordinary premises (Kp ), which
A1
PG
are uncertain assumptions and can be attacked by otherreturn_productarguments, and axioms (Ka ), i.e. certain premises, thatG(improve_cs)
contributes_tocannot be attacked. Part of the knowledge base K of our
improve_cs
PG
running example can be formalized as follows:E
2

Ka : {E(CRM expert), E(sales clerk)}
Kp : {G(increase profit),c1 : improve cs contributes to increase profit,c2 : return product contributes to improve cs}
Thus, the evidence CRM expert and sales clerk are theaxioms of the knowledge base, meaning that they cannot
be attacked. The contributes to formulas have been named
c1 and c2 for convenience. Note that c1 and c2 are both
directly taken from the contribution and decomposition
links in GRL from Figure 1.
1 Note that in our framework we do not distinguish between softgoals
and hard goals.

G(increase_profit)

improve_cs
contributes_toincrease_profit

E(sales_clerk_evidence)

E1

E(CRM_expert_evidence)

Fig. 2: An argument based on the GRL in figure 1
In our framework, an argument is a tree. The leaves ofthis tree (i.e. the argument’s premises) are elements of theknowledge base K and all other nodes are inferred using an
inference rule in R. The root of the tree is the argument’s
conclusion, and subtrees are called subarguments. As an
example, assume that we have strict inference rules frompropositional logic and defeasible inference rules PA, PG,
EV1 and EV2 discussed above.

Given these inference rules and the set K, we can For example, whilst normally we would consider expert
construct the argument in Figure 2. In this figure, statements to be good reasons for believing something (cf.
the arrows stand for inferences, where the inference inference rule EV1 ), an exception to such an “inference
rule is indicated next to the arrow. Note that argu- from expert opinion” occurs when we have an argument
ment A1 can be extended into arguments for G(bought) that the expert is in fact biased (for example, because heor G(¬damaged) using the contributes to connective would like to sell a specific CRM package that facilitates“return product contributes to improve cs”.
product returns). Figure 4 shows this counterargument
Since the conclusion of argument A1
is to the expert argument. Note that not the premises nor
G(return_product), using the PA inference rule we conclusion is attacked, but rather the inference itself.
can then further discuss which actions we have to
D. The Acceptability of Arguments
take to realize these goals. There is, then, an exact
Given a collection of arguments and their attack relations,correspondence between the GRL model from Figure 1
and these arguments: the goals, actions and links in GRL we can use various argumentation semantics [7] to determine the acceptability of the arguments. Such semantics
are explicitly represented as elements of the argument.
abstract away from the internal structure of the arguments
C. Attacks Between Arguments
and consider only arguments and their attacks. Informally,
An important feature of argumentation is that argu- all arguments that are not attacked by any argument are IN.
ments can be attacked by counterarguments. A common Arguments that are attacked by an argument that is IN are
way to attack an argument is to contradict one of its OUT, and arguments that are only attacked by arguments(sub)conclusions. For example, it was argued that allowing that are OUT are IN as well. Otherwise, an argument iscustomers to return products would cost the company UNDECIDED. We have visualized the arguments of oura lot of money. This argument can be formalized as running example in Figure 5, where A1 and A3 are IN,
A2 in Figure 32 . The argument A2 attacks the original and A2 is OUT. Note that if argument A3 would not exist,
argument A1 from Figure 2 and vice versa (rendered as both A1 and A2 would be UNDECIDED.
dashed lines with an open arrowhead): G(return product)and ¬G(return product) are obviously contradictory. In
A1
A2
A3
the discussion, however, argument A2 was counteredwith evidence of statistics (stats evidence) showing thatfew people return products (few product returns). This
Fig. 5: The acceptability of argumentsallows us to construct argument A3 , which attacks A2 ,
Since
the
elements of the arguments correspond to the
because we can say that the fact that there will be few
elements
ofthe
GRL model, we can determine the status
returns contradicts the belief that allowing such returns
of
these
GRL
elements
based on the underlying rationalizawill cost a lot of money.
tions, which are made explicit in the argumentation theory.
In this way, the argumentation theory provides a formal
few_product_returns
G(return_product)
ØG(return_product)
grounding for GRL, since we can apply Dung’s [7] calculus
for determining the status of arguments to elements of
A1
GRL. Thus, in our example, the goal G(return_product)(fig. 2)is IN, while the goal ¬G(return_product) is OUT (cf. theG(increase_profit) B(cost_money)E(stats_evidence)
the GRL model in Figure 1).
A3
A2
Fig. 3: Attacking arguments

IV. The Metamodel

Figure 6 depicts the metamodel linking the main elements of our formal framework to the main GRL elements.
improve_cs contributes_to
The part below the dashed horizontal line depicts GRL
expert_biased
increase_profit
elements. A GRL Diagram (bottom) contains zero or more
IntentionalElements, either a Goal, a Softgoal, a Task, or aBelief. An ElementLink is either a Contribution, a Decompoexpert_sells_CRM_package
E(CRM_expert_evidence)
sition, or a Dependency and contains an IntentionalElements
as source and as target.
Fig. 4: Providing an exception to an inference
The part above the horizontal dashed line depicts theconcepts
we introduced in the previous section. The topAnother way to attack an argument is to deny anleftelement
Argument (see Section III-B) can attack otherinference step by giving an exception to an inference rule.
arguments (Section III-C) and generalizes both a Formula
2 Note that in A we use an unspecified defeasible inference ruleand an Inference. That is, both these elements can be2
saying roughly that ‘if something costs money and we want to increase
arguments. A formula has an AcceptStatus (Section III-D).
our profits, we should not do it’.
An Inference is from a set of Arguments as premise and aFormula as conclusion. The InferenceType is either strict or
defeasible (Section III-A). A Formula is either a Modality(modal formula), a Proposition, a BinaryOperation, or a
Negation (negated proposition). The Modality can be either
B (belief), G (goal), E (evidence), or A (action). A
BinaryOperation is either a Disjunction, a Conjunction, or acontributes to Connective (III-A).
attack0..*
1..* premise

Argument
name: String

FormulaisAtom: bool
status: AcceptStatus1
conclusion

AcceptStatus

InferenceType

IN
OUT
UNDECIDED

StrictDefeasible

Inference

InferenceRule

type: InferenceType

name: String
ModalityType

modal: ModalityType

0..1

1

Proposition

B
G
A
E

Negation

name: String

left 1

1 right

BinaryOperation

Goal/softgoal  G
Belief  B
Task  A
Disjunction

Conjunction

0..1

Connective

Goal

Softgoal

Task

Beliefname: String

name: String

name: String

name: String

IntentionalElement
name: String
decomp: DecomType
LinkType
Contribution
Decomposition
Dependency

1 source
1 target

GRL Argumentation

Modality

1

ElementLink
type: LinkType

DecomType
AND
OR
XOR

GRL Diagram

Fig. 6: The Metamodel
Finally, the red arrows depict how the two metamodels
are integrated. The left arrow connects a GRL IntentionalElement with an argumentation Modality, where themapping is denoted with red text. Thus, an intention
element traces to an argument, which is always a modal
formula. The right arrow connects the GRL ElementLink
with the argumentation Connective (i.e. a contributes toconnective). Thus, an arrow between elements of a GRL
model corresponds to contributes to connectives in theargumentation framework.
V. Research Agenda
A. Extension, Implementation and Evaluation
The current paper briefly describes how formal argumentation could be used to capture discussions about goals
and evidence. The first objective of our future work is toexpand on this basis by, for example, capturing specificargumentation patterns and critical questions for evidencebased requirements engineering in our formal framework(cf. [3], [5]). URN has been further extended with, for example, Legal-URN [9], which deals with legal compliance.
Through the integration with the argumentation framework,organizations can then capture the evidence for selecting
different alternatives interpretation of legal text [10], andthis evidence can in turn be presented to the auditor as a
proof of compliance.
GRL has an open source Eclipse-based tool support
called jUCMNav [2]. Based on the metamodel of theprevious section, import-export functions can be defined in
jUCMNav, which, for example, allow us to convert practicalreasoning arguments from the design discussions to a GRL
model. These arguments can be built using the Argument
Web tools [4]. For example, OVA3 can be used to quickly
build arguments such as the ones in Figures 2 and 3, andTOAST4 can be used to evaluate the acceptability of thesearguments. The Argument Web is based on a standard andwell-defined argument ontology and include web services
for importing and extracting argument data in a JSON
linked data format.
In order to demonstrate the applicability of our approachand to evaluate our method, we aim to complete our
implementation and test factors such as scalability andusability. This will also allow us make the connection
to the practical aspects of the requirements engineering.
One of the core questions here is what the benefits ofusing argumentation are compared to not having a formal
framework and, for example, keeping information about
rationalizations and evidence in unstructured comments.
B. Rationalization of Use Case Maps
Recall from the introduction that GRL is part of URN,
an ITU-T standard which combines goals (modeled byGRL) and scenarios (modeled by Use Case Maps (UCM))
in one single notation. Although we have primarily focusedon GRL in this paper, we aim in future work to extend
our analysis to UCM as well. UCM is used to model
scenarios, business processes and functional requirements,
and provides means for reasoning about scenarios by establishing URN links between intentional elements (such assoftgoals and goals) in GRL and non-intentional elements in
UCM. Thus, with capturing evidences and rationale behindalternatives, it is possible to reason about the different
business processes and scenarios in the organization.
We believe that this a good fit with the work on stories
and argumentation. In [6], a hybrid formal theory is
introduced that allows one to reason about different stories
using arguments. The stories are used to causally explain
the most important facts of a case and arguments basedon evidence are used to support and attack these stories
and each other. Since these stories are very similar to usecase maps, we can apply the same techniques to reason
3 http://ova.arg-tech.org/
4 http://toast.arg-tech.org/

about the plausibility of different use case maps, based onunderlying goal models, which are in turn rationalized byarguments and evidence.
VI. Related work
Several evaluation algorithms have been developed tocompute the satisfaction level of high-level fuzzy goals basedon the selection of lower level goals and tasks [2]. Gross
and Yu [11] explore the use of goal-oriented approaches toprovide links between business goals, architectural design
decisions and structure in a systematic way. They also
provide a qualitative evaluation algorithm for goals. However, as mentioned before, we assess not the satisfiability ofgoals given certain tasks that are performed, but rather theacceptability of the goal model itself given the discussion
between the stakeholders.
Argumentation has been applied to requirements engineering in other work. For example, both Haley et al. [12]and Franqueira et al. [8] use structured arguments tocapture design rationales and to show that a system cansatisfy its security requirements, respectively. The argumentative part of their work, however, does not include formal
semantics for determining the acceptability of arguments.
Furthermore, while arguments are included in the design
process, there is no explicit trace from arguments to goals
tasks. Jureta et al. [14] propose a method to guide thejustification of goal modeling choices, and include a detailed
formal argumentation model that links to goal models.
Although their formalization is comparable to ours, we
believe that our work paves the way for further enrichment,
standardization and implementation of argumentation ingoal models. Our argumentation method will draw fromboth [3] and [5] in order to provide detailed techniques
for reasoning with evidence about goals and actions, suchas critical questions designed to probe and assess goalbased and evidential reasoning. GRL is part of an accepted
standard [13], and ASPIC+ has become a mainstay in AI
research on computational argumentation, fueled also by
its well-defined connections to Dung’s semantics [7] andthe Argument Web [4]. Finally, both GRL and ASPIC+
have existing implementations available, which facilitatesfurther tool development. We have presented initial resultsof this paper in [17].
VII. Conclusion
We propose a framework for traceability of GRL elements
to the arguments and evidence of the stakeholders. First, we
extend the ASPIC+ framework for formal argumentation[15] with rules for practical and evidential reasoning. This
allows us to formally capture evidence-based discussions
between stakeholders and subsequently determine theacceptability status of arguments’ conclusions (e.g. goals,
beliefs) using argumentation semantics [7]. Next, we integrate the formal argumentation model with GRL elements
using a UML metamodel. Arguments in our framework
are built from propositions, which are either goals, actions,connectives, beliefs, or evidence. These elements correspondrespectively to softgoals and goals, tasks, links between
intentional elements, and beliefs. Thus, we can computethe acceptability of a GRL model given the evidence anddesign arguments given by the stakeholders. Finally, we
set out a research agenda describing future work consistingof an extension to argumentation patterns and criticalquestions, an extension to Legal-URN, an implementation
to evaluate our framework, and the rationalization of Use
Case Maps.
Acknowledgements
This work has been partially funded by AFR - PDR
grant #5810263 and the National Research Fund (FNR),
Luxembourg, RationalArchitecture project.
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