=Paper=
{{Paper
|id=Vol-1402/id9
|storemode=property
|title=An Initial Approach to Reuse Non-Functional Requirements Knowledge
|pdfUrl=https://ceur-ws.org/Vol-1402/paper9.pdf
|volume=Vol-1402
|dblpUrl=https://dblp.org/rec/conf/istar/VeledaC15
}}
==An Initial Approach to Reuse Non-Functional Requirements Knowledge==
https://ceur-ws.org/Vol-1402/paper9.pdf
Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
An Initial Approach to Reuse Non-Functional Requirements
Knowledge
Rodrigo Veleda, Luiz Marcio Cysneiros
School of Information Technology – York University, Canada
rveleda@yorku.ca, cysneiro@yorku.ca
Abstract. Non-Functional Requirements (NFR) can be seen as qualities that software
should deliver to cope with the stakeholders’ demands. NFRs are fuzzy in nature and
hence hard to identify. Despite the fact that both developers and users may value
NFRs, they frequently do not identify the need for an NFR. Even when an NFR is
identified as required, possible solutions to implement this NFR may be hard to fig-
ure out. Furthermore, interdependencies among NFRs may implicate that a solution
for one NFR may, at the same time, bring synergy to one NFR while conflicting with
another. One approach to deal with that is to use Softgoal Interdependency Graphs
(SIG) to capture knowledge describing alternatives to implement NFRs. We have ob-
tained empirical evidence that using catalogues can help eliciting NFRs despite the
fact that catalogues do not scale too well. To address this question, we have investi-
gated the use of ontologies and semantic web techniques to represent SIGs in a ma-
chine readable format. We have produced a tool (NDR) that starts to use these con-
cepts. In its current form, the NDR tool only allows very basic queries done manual-
ly. The NDR tool is part of the NDR framework which will facilitate the reuse of
NFR knowledge on Alternatives to incorporate NFRs into the design of target sys-
tems.
Keywords: Non-Functional Requirements, Reuse, Knowledge
1 Introduction.
Requirements engineers have to address both functional and non-functional require-
ments to develop software systems [1]. Functional requirements are responsible to repre-
sent what the system is capable of in terms of available features. On the other hand, non-
functional requirements are known to represent quality attributes [2, 3]. These quality
characteristics include privacy, performance, usability and other similar aspects related to
the quality of a software system.
The first challenge for eliciting NFRs lies on the fact that they are fuzzy in nature and
quite frequently are missed both by software engineers and stakeholders. Furthermore,
choosing one solution to implement one NFR might bring synergies and perhaps most
importantly conflicts to another NFR bringing the perception that one NFR can rarely be
expected to be 100% satisfied. We use the term satisfice [4, 5] to represent the idea that
NFR is satisfied within acceptable limits.
Some works have proposed the use of catalogues representing knowledge to satisfice
NFRs as a way of helping not only to elicit NFRs but also to reason about the complexity
involved in choosing alternatives to satisfice an NFR [4], [6]. In fact, empirical work has
suggested that the use of catalogues can contribute to avoiding omissions and missed con-
flicts, despite the fact that SIGs do not scale too well [6].
These catalogues are implemented using Softgoal Interdependency Graphs (SIG) [4].
SIG catalogues promote a graphical representation of essential quality characteristics for
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Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
satisficing a given non-functional requirement. SIGs also demonstrate possible tradeoffs
among non-functional requirements in the target system.
In this paper, we discuss an ongoing research by introducing the NDR Tool. The NDR
Tool is currently under development, and it is part of the NDR Framework which aims to
facilitate the reuse of non-functional requirements knowledge captured in SIGs. Our appli-
cation strives the extraction of present knowledge from SIGs and represents it in a machine
readable format using ontologies and semantic web techniques [7, 8] Furthermore, the tool
proposes the storage of collected knowledge within an ontology repository that currently
follows the proposed Non-functional requirement and Design Rationale (NDR) Ontology
[7]. We are developing the NDR tool to store as many alternatives as possible to satisfice
NFRs. It will also use RDF [9] queries for retrieving alternatives for one specific problem,
allowing software engineers to select one alternative and import this alternative to its i*
models representing the target system. This work depicts the tool's currently available
features and also denotes the potential challenges and future tasks for implementation.
The remainder of this paper is structured as follows: Section 2 illustrates the related
work. Section 3 describes the objectives of our research and its scientific contribution.
Finally, Section 4 presents the on-going and future work.
2 Related Work
Some works [10, 11] focus on experienced-based elicitation and recommendation for
the use of non-functional requirements in software service. Others [12, 13, 14, 15], aim the
use of ontologies to assist non-functional requirements elicitation. Nevertheless, none of
these proposed works addresses the challenge of investigating the potential tradeoffs
between multiple non-functional requirements. Nor they interact with i* tools to facilitate
the reuse of the knowledge.
Considering the use of SIGs aiming the reuse of knowledge, Sancho et al. [16] proposes
an ontological database. Their work consists of two ontologies both written in OWL [17]:
The NFR ontology and the SIG ontology. The NFR ontology explains the NFRs concept
and relationship among them. The SIG ontology depicts SIG constructs and their
relationships. We have identified two shortcomings within this approach. First, the SIG
ontology does not define any class to describe the Correlation interdependency between
Softgoals therefore limiting reasoning involving more than one NFR. Second, it does not
enforce the use of proper kind of Softgoals as parent and offspring of each Refinement.
The NDR ontology is based on this work.
Hazeem et. al [13] introduced the ElicitO. ElicitO is an ontology-based tool that
supports non-functional requirements elicitation by providing a knowledge base that
relates non-functional requirements and its associated metrics. Also, Najera [14] highlights
an approach that uses OWL and RDF targeting the representation of i* variants.
Unfortunately, the reuse of non-functional requirements knowledge is not tackled in this
work nor is cited as future work.
3 Research objectives and scientific contribution
Our long-term goal is to develop a framework that can help software engineers to elicit
and model non-functional requirements empowered by the knowledge that has previously
been elicited and validated. We believe that the use of a well-defined knowledge base
could play an essential role to achieve this goal. Therefore, our environment will emerge
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Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
as the result of further developing our ontology and tools to store and retrieve knowledge
on satisficing non-functional requirements.
Hence, our first goal is to develop further the NDR tool to efficiently store NFR
knowledge while allowing querying at different levels of granularity for retrieving existing
information. .
The next step will be to develop mechanisms to import and export knowledge from and
to other Tools that support i*. Techniques to import SIGs from i* tools will be implement-
ed as well as the ability for choosing alternatives from existing SIGs in the NDR tool to be
imported into i* models expressed in tools such as jUCMNav [18].
At first, this environment will only be open to accepting queries from the academic
community. However, in a near future we envision to accept contributions from other re-
search groups working with NFR knowledge to enrich the existing knowledge base. At the
same time, we will also allow members from the industry to query the knowledge base and
submit comments with their perception. At a later stage, contributions to add to the
knowledge base will be accepted from a broad audience.
4 Ongoing and Future work
4.1 NDR Ontology
The NDR Ontology proposed in [7] represents NFRs and design argumentative ra-
tionale knowledge in a machine-readable format. This representation follows the proposed
standards of OWL. Therefore, each examined SIG catalog is converted into semantic
graphs, establishing new sets of instances of NDR Ontology and expressing a machine-
readable form. In operational terms, RDF is widely used to describe ontologies (mainly at
semantically enriched Web sites). RDF encodes information as triplets (resources) that
relate a property to other resources or plain literal data. Thus, RDF models are directed
labeled graphs that allow representing meaningful contents. RDF Schema (RDFS) [19]
allows describing properties and classes of RDF resources and supports a generalization
hierarchy for properties and classes. As a short-term goal, we will further develop the on-
tology and the tool capability of producing the necessary SPARQL queries [20] from user-
friendly dialogs.
In our proposed approach, we envision to provide the NDR Ontology available within
the NDR Tool in a cloud environment. We believe that making our proposed approach
available in a cloud will facilitate not only its use by different audiences but also the op-
portunity to receive contributions to enlarge the knowledge base that will be available.
Currently, we have implemented a proof of concept version of the NDR Ontology on
our cloud ontology repository. In order to have a user-oriented layer displaying details of
the target ontology graph-
ically, we have integrated
our platform with Web-
VOWL [21]. The Web-
VOWL is a web-application
that implements the Visual
Notation for OWL Ontolo-
gies (VOWL) [22]. A graph-
ical visualization of the
NDR Ontology version is
currently implemented in Fig. 1. The NDR Framework Architecture
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Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
our platform1. We are in the process of importing SIG catalogs into the target environment
to also demonstrate the knowledge expansion graphically.
4.2 NDR Framework Conceptual Architecture
To maintain and assure a reliable integration between SIGs developed with i* Tools and
its conversion into ontology knowledge, we propose the NDR Framework. Figure 1 illus-
trates its conceptual architecture.
The NDR Tool is mainly composed of a knowledge base and an ontology repository.
As mentioned in Section 4.1, currently, only the NDR Ontology is implemented within the
platform. We aim to develop a generic ontology repository that can be instantiated in do-
main specific ontologies to provide extensibility of our platform. In other words, the NDR
Tool will be able to handle several ontologies, preserving a valuable and vast knowledge
base.
Aside from holding ontologies, the NDR Tool will also be in charge of handling the
conversion of SIG catalogs into ontology instances. Besides the execution of parsers de-
signed for each type of supported SIG and ontology, our platform will detect if the artefact
that is being provided contains relevant knowledge based on definitions manually defined
by repository administrators. An approach based on Open-Source concepts will be devel-
oped to handle this.
Access to the knowledge contained in the NDR Tool will be possible through the use of
web services. We envision to implement RESTful web service endpoints that can be in-
voked externally by third-party i* applications. Essential features such as artefact importa-
tion and knowledge retrieval will be implemented within these services, facilitating future
integrations.
To illustrate an appropriate real-world example of the applicability of the NDR Frame-
work, we portrait a SIG representing the non-functional requirement of Transparency as
demonstrated in Figure 2 is uploaded into our framework. The NDR Tool will extract the
knowledge in the provided SIG based on the settings manually defined by the repository
administrator.
Then, the tool will convert the selected knowledge into a machine-readable format, fol-
lowing the NDR
Ontology standards.
As it is noticeable
in Figure 2, the
visual information
represented in the
SIG illustrates the
scalability problem
mentioned earlier in
this paper. As more
details such as cor-
relation and decom-
positions are added
into the SIG, its
understandability
Fig. 2. Transparency SIG
1
http://132.206.206.138:8888/
28
Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
and clarity becomes affected.
By having the knowledge ready in a machine-readable format, the NDR tool will man-
age to update the ontology with new individual instances and persist the modifications into
a database. At this point, the extracted knowledge from the Transparency SIG is available
for reuse.
The reuse of stored knowledge will be possible by accessing web service endpoints.
Once an endpoint is reached, the NDR Tool will handle the request by applying the
SPARQL queries over the stored knowledge. For instance, a software engineer wants to
know which non-functional requirements are directly related to the satisficing of Transpar-
ency. After receiving this request, the NDR Tool will execute an SPARQL query similar
to the following:
SELECT DISTINCT ?interlinkId ?softgoalParent ?softgoalSpring
?contributionKind WHERE {?interlinkId rdf:type ndr:Correlation.
?interlinkId ndr:correlationHead ?softgoalParent. ?interlinkId
ndr:correlationTail ?softgoalSpring. ?interlinkId ndr:contributionKind
?contributionKind.}
Basically, this query is selecting all the correlations that somehow affect the satisficing
of the Transparency softgoal. Table 1 demonstrates the internal result of this query execu-
tion.
As an outcome of this pro-
Table 1. SPARQL query execution result cess, the NDR Tool retrieves the
requested information and the
interlinkId softgoalParent softgoalSpring contribution
ndr:UH_correlation2 ndr:Informativiness ndr:Anonymity ndr:Hurt
result is ready to be sent back to
ndr:UH_correlation7 ndr:Integrity ndr:Data_Share_and_Use ndr:Help the user. It is noteworthy to
ndr:UH_correlation1 ndr:Usability ndr:Anonymity ndr:Hurt mention that the possibility of
ndr:UH_correlation6 ndr:Completeness ndr:Data_Share_and_Use ndr:Help
having results in a graphical way
ndr:UH_correlation4 ndr:Operability ndr:Data_Share_and_Use ndr:Help
ndr:UH_correlation8 ndr:Decomposability ndr:Data_Share_and_Use ndr:Help
instead of machine-readable
ndr:UH_correlation5 ndr:Adaptability ndr:Data_Share_and_Use ndr:Help format will depend on the level
ndr:UH_correlation3 ndr:Availability ndr:Data_Share_and_Use ndr:Help of integration with a given i*
tool.
4.3 jUCMNav Integration
We aim at integrating the NDR Tool with jUCMNav to have SIG catalogs import-
ed/exported into/from our platform.
jUCMNav is an open-source modeling tool that supports the i* Framework. One of the
reasons that we decided to integrate our approach with jUCMNav is its extensibility. An-
other reason is that jUCMNav is a cross-platform application endeavor. It is well docu-
mented and presents a steady process of growth. Lastly, by integrating the NDR tool to
jUCMNav, we can provide a graphical visualization to resulting SIGs from queries. Alt-
hough we will be mainly focusing on jUCMNav at first, all our efforts will keep in mind
the need to develop an interactive approach that can work with as many i* tools as possi-
ble.
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