Semantic Enrichment and Veriﬁcation of Feature Models in DSPL

Thalisson Oliveira, Rossana M. C. Andrade

∗

and Windson Viana

Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Cear

a, Fortaleza,

Cear

a, Brazil

Keywords:

Dynamic Software Product Line, Context-awareness, Feature Model, Automatic Veriﬁcation Tool, Ubiquitous

Software, Semantic Enrichment.

Abstract:

Dynamic Software Product Lines (DSPLs) support the development of context-aware systems, which use

context information to perform adapted services aiming to satisfy user’s needs. Feature models (FM) represent

system similarities and variability in DSPL. However, some FM representations are limited in expressiveness.

For example, relevant domain aspects (e.g., context-aware feature that implements a particular use case) are not

described in FM. This research proposes an approach based on an OWL-DL ontology to add semantics to FM.

It also provides automatic veriﬁcation of the correctness and consistency of these models. We implemented

this approach in a feature model design tool called FixOnto. Our ﬁrst evaluation results showed that the use of

ontologies brings beneﬁts such as improvements on SPL information retrieval, and inference and traceability

of the features, use contexts, and SPL artifacts.

1 INTRODUCTION

Currently, powerful mobile devices with several sen-

sors enable the development of context-aware appli-

cations, i.e., applications that can perceive the envi-

ronment and adapt themselves to satisfy the user’s

needs (Marinho et al., 2013). However, due to self-

adaptation complexity, the development of context-

aware systems becomes a challenging activity. Soft-

ware engineers have to predict context variations, in-

cluding exceptional situations and have to code con-

sistent adaptation methods (Marinho et al., 2012).

Software Engineers can use SPL to systematize

and maximize reuse in context-aware systems de-

velopment (Marinho et al., 2013). Dynamic Soft-

ware Product Lines (DSPLs) are SPLs that include

mechanisms to change variants in self-adaptive sys-

tems at runtime (Hallsteinsen et al., 2008), support-

ing context-aware systems development. In (Mar-

inho et al., 2012), authors propose the concept of

Context-Aware Software Product Lines (CASPL), a

subset of DSPL. They introduce Context-Aware Fea-

ture Models (CAFM). A CAFM is composed of a sys-

tem model (SM) and a context model (CM). CASPL

are our research target.

In CASPL, feature and context modeling are the

∗

CNPq Productivity Scholarship in Technological De-

velopment and Innovative Extension (DT-2)

main activities involved in their design (Dermeval

et al., 2015). CAFM represents feature variability

and also predeﬁned context situations and adapta-

tions. Context-aware systems modeling is an error-

prone task, as a consequence, a CASPL can derive

invalid products. Thereby, veriﬁcation of CAFM is

a necessary step. However, manual veriﬁcation in

complex systems, such as context-aware systems, and

in large-scale is infeasible (Benavides et al., 2013).

Thus, there is a necessity for automatic veriﬁcation of

feature models to prevent inconsistencies.

To motivate our approach, let us take the domain

of Mobile Visit Guides (MVG). We considered it as

a family of self-adaptive products, in which applica-

tions can display content (e.g., text, image, and video)

according to the user’s location, and his device con-

ﬁgurations and status. In our example, the domain ex-

pert should associate Video Display feature to user’s

location and his device battery level. To perform this

kind of adaptation, the domain expert must select the

Message Exchange feature since he has modeled it as

a mandatory feature. However, the domain expert can

model a context situation that not choose the Message

Exchange feature, then, the SPL will derivate a prod-

uct with an inconsistent adaptation according to the

FM.

Regarding this scenario, this work proposes an ap-

proach and a tool for adding semantic and automatic

veriﬁcation of correctness and consistency of CAFM.

542

Oliveira, T., Andrade, R. and Viana, W.

Semantic Enrichment and Veriﬁcation of Feature Models in DSPL.

DOI: 10.5220/0006376105420549

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 2, pages 542-549

ISBN: 978-989-758-248-6

OWL-DL ontologies

and inference mechanisms are

the core assets of our approach. The use of ontologies

adds new capabilities to DSPL such as improvements

on information retrieval and traceability. In our ap-

proach, a DSPL modeler can perform domain model-

ing, associate core assets to the FM, and also performs

FM veriﬁcation.

We evaluated our tool with the following research

questions (RQ) in mind. RQ1: How to semantically

enrich a DSPL (associating features and contexts to

core assets) and ensure that the mapping remains cor-

rect after this association? RQ2: How to make infor-

mation retrieval and traceability in DSPL easier?

2 BACKGROUND

As the main goal of our research is to provide a so-

lution for semantic enrichment and CAFM veriﬁca-

tion, ﬁrstly, details of CASPL and CAFM are shown

in this section. Secondly, semantic enrichment ap-

proaches are presented. Finally, CAFM veriﬁcation

is discussed.

2.1 Context-aware SPL

Context-aware SPL focus on deriving products that

can provide services according to user’s needs, ob-

serving the environment around them (Costa et al.,

2015). Thus, CASPL is an SPL that promote reuse

in the development of context-aware applications.

A key component of CASPL is the Context-aware

Feature Model (CAFM), which combines traditional

feature model with a context model in order to pro-

vide a better SPL description. CAFM is composed by

four diagrams: (i) System Model (SM), representing

SPL similarities and variability, (ii) Context Model

(CM), representing the known contexts, (iii) compo-

sition rules (CR), related to SM and (iv) adaptation

rules (AR) or context rules, related to CM (Marinho

et al., 2012).

System Model is a tree-like diagram containing

a unique root, which represents the domain. The

reminder nodes correspond to domain features and

edges describe the hierarchical relationships between

these features (Marinho et al., 2012). One example

of SM is found in Figure 1 (a). MobileGuide root

node corresponds to the domain being represented.

Message Exchange node is a mandatory feature. Ex-

change Type node is a variation point, which can be

Asynchronous or Synchronous. Security and Privacy

are optional features.

http://www.w3.org/TR/owl-features/

Figure 1: MobiLine (a) System Model fragment and (b)

Context Model fragment.

Context Model has four levels. The ﬁrst level

is the modeled context and contains only one node.

Nodes in the second level represent the Context En-

tities and nodes in both third and fourth levels show

Context Information and Context Attributes, respec-

tively (Marinho et al., 2012). In Figure 1 (b) there is

an example of CM. The root node Context represents

one context that needs to perform adaptations through

context entity. Context entity (MobileDevice) is the

entity observed by the context in question, and it pro-

vides context information. It represents which entity

information will be read (e.g., battery level and Lo-

cation). In the fourth level, there are the context at-

tributes, which are possible values to context infor-

mation (e.g., battery level can be 0 to 100%) and are

not represented in the ﬁgure.

Composition rule is formally speciﬁed as an im-

plication of an antecedent expression to a consequent

expression, being each expression one propositional

formula over a set of features and attributes contained

in the System Model (Marinho et al., 2012). In Table

(1), an example of rule expression is presented. The

rule CR1 shows that the presence of Synchronous Ex-

changeType implies in the use of Security feature.

Adaptation rule also is formally speciﬁed in (Mar-

inho et al., 2012), as an implication of a context ex-

pression (CE) to a system expression (SE). An event

can be a state or combination of states of a system.

One action can also be a new state or a combination

of states in the system. For instance, if the battery

level reaches a value lower than 10%, the application

disables the video displaying. In (1), one example of

adaptation rule expression is presented. The rule AR1

Semantic Enrichment and Veriﬁcation of Feature Models in DSPL

543

means than if the location of the device is an insecure

area implies in the use of Privacy feature.

Table 1: Example of Rules proposed in (Marinho et al.,

2012).

Rule Expression

CR1 ExchangeType.Synchronous =⇒

Security

AR1 (MobileDevice.Location =

UnsecureArea) =⇒ Privacy

One example of CASPL is MobiLine (Marinho

et al., 2013), which is a CASPL for the mobile and

context-aware domain. This domain holds adaptabil-

ity as a principle and suggests that during execu-

tion time, the applications should be able to adapt

themselves according to the changes in user’s context

(Marinho et al., 2013). Figure 1 contains a fragment

of Mobiline (Marinho et al., 2013), which is an SPL

for the domain of mobile and context-aware applica-

tions. For instance, the Mobilide Guide represents the

domain, and Message Exchange feature is necessary

to display information in the mobile guide.

The CASPL concepts explained in this section

guided the solution proposed in this work. We used

MobiLine as case study and examples showed in Fig-

ure 1, illustrated system model and context model

in CAFM and the Extended FM notation (Benavides

et al., 2013) was used. Figure 1, showed previ-

ously, illustrated system model and context model

in CAFM and the Extended FM notation (Benavides

et al., 2013) was used.

2.2 Adding Semantics to SPL

Software Product Lines are well known for provid-

ing a high level of reuse. SPL core assets contain

the set of artifacts used to build a product. Feature

models are commonly used to represent SPL and can

be used to group artifacts from core assets and help

on traceability. In (Filho et al., 2012), the authors

introduce the concept of Semantic Software Product

Line (SSPL) as an SPL where reusable core assets and

business artifacts are related to the domain model and

can be expressed using one or more ontologies. Thus,

ontologies increase knowledge expressiveness in SPL

and allow infer new information and implicit relations

by using inference algorithms provided by reasoners.

Feature model can be a start point for adding se-

mantic to an SPL. In our work, add semantics means

to associate core assets to FM, making this FM more

expressive. For instance, relationships between a fea-

ture and other SPL artifacts (e.g., source code and re-

quirements documents) can be speciﬁed. The authors

of (Filho et al., 2012) propose a process that involves

two main steps: (i) transformation of a feature model

in an ontology (OWL DL), performed automatically

by a tool called Fea2Onto, and (ii) the addition of new

relations with external artifacts in a process guided by

a top ontology called SPLiSEM. When the process is

ﬁnished, a more expressive model of SPL is produced.

2.3 Feature Model Veriﬁcation

Feature Models (FM) are widely used to represent

SPL, and it is relevant to assure the model correct-

ness and consistency. Regarding model veriﬁcation

of CASPL, (Marinho et al., 2012) propose a formal

process to check correctness and consistency of a

Context-aware Feature Model (CAFM). That process

describes a set of well-formedness rules for Feature

Model and Context Model. They also provide ver-

iﬁcation if the model is in accordance with compo-

sition and adaptation rules. In Table 2, the descrip-

tion of a subset of Well Formed Composition Rules

(WFCR), Well Formed Adaptation Rule (WFAR) and

Inter-rules Consistency (IRC) proposed in (Marinho

et al., 2012) are listed.

Table 2: Subset of Rules proposed in (Marinho et al., 2012).

Rule Description

WFCR1 Features referenced in a Composition

Rule should be either an optional

feature or an attribute feature.

WFAR1 Features referenced in the System

Expression should be either an

optional feature or an attribute feature

and should be owned by the SM

IRC1 CRs deﬁned for a CAFM should be

consistent with each other

Based on (Marinho et al., 2012), a tool was devel-

oped in (Costa et al., 2015) and provides CAFM mod-

eling, veriﬁcation of feature model well-formedness

and consistency and also a simulation of product

derivation. The tool in (Costa et al., 2015), Marinho

et. al approach (Marinho et al., 2012), and seman-

tic enrichment approach (Filho et al., 2012) were the

start point of our proposal.

3 AN APPROACH FOR

SEMANTIC ENRICHMENT

AND VERIFICATION OF CAFM

This work proposes an approach that permits context-

aware feature modeling, semantic enrichment of these

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

544

models, and automatic feature model veriﬁcation.

Like a traditional SPL, CASPL also takes bene-

ﬁts from the integration of semantic enrichment and

model veriﬁcation. We aim at assist CASPL model-

ers and all stakeholders involved in the context-aware

applications development.

3.1 Design Principles

Our approach is composed of four main steps: (a)

feature modeling, which includes semantic enrich-

ment, (b) automatic model transformation, (c) auto-

matic veriﬁcation of feature model, and (d) semantic

search.

In the ﬁrst step (Feature Modeling and Seman-

tic Enrichment), a domain specialist performs fea-

ture modeling, context modeling, and associates these

models to domain artifacts. In the feature modeling

process, the constraints between features and context

situations are modeled. A CAFM is the output of this

ﬁrst step.

In the second phase (Automatic Mapping), the

CAFM is transformed in an OWL DL ontology auto-

matically. This ontology represents features, contexts,

rules, and artifacts present in the CAFM. In this pro-

cess, the transformation requires a top ontology con-

taining CAFM concepts (e.g., feature, context situa-

tions, rules). The section 3.2 details this top ontology.

A reasoner analyzes the OWL version of the

CAFM in the “Model Veriﬁcation” phase. The rea-

soner evaluates the model correctness and consis-

tency. This veriﬁcation uses the rules proposed in

(Marinho et al., 2012). For instance, if the ontology

is valid (all derived product obey the rules), then it is

ready to last step (semantic search).

The fourth step (Semantic Search) allows to do-

main specialist performing queries over the improved

models. It can retrieve feature’ information and

traces, relationships between context scenarios and

model artifacts. An ontology reasoner is a support

for this kind of query. One example is the DL Query

language provided by Prot

We implemented a tool called FixOnto, which per-

mits both feature and context modeling. FixOnto

also allows semantic enrichment making association

among features and both use case and test cases.

Moreover, it is possible to perform model veriﬁcation

and information retrieval.

3.2 Top Ontology for CAFM

Figure 2 shows a fragment of the top ontology rep-

resenting the CASPL. The System Model (SM) ac-

http://protege.stanford.edu

Figure 2: Top ontology representing System and Context

Models in CASPL.

counts for the variability and commonalities between

features of the domain modeled. Thus, it is important

that concepts like (mandatory feature) and (optional

feature) are presented in the ontology. The context is a

specialization of the system model, which represents

the context situations in the domain, containing enti-

ties and contextual information. Therefore, the ontol-

ogy also contains these concepts.

CASPL is composed by elements, which are re-

lated to system diagrams and context diagrams (Fig-

ure 2). The system model consists of a feature hi-

erarchy. In this top ontology, the Feature subclasses

can have self-relationships. For instance, an individ-

ual of Mandatory Feature can have a subfeature and

individual of Variation Point. The opposite is also

possible. The domain specialist relates features to

other features by using the properties hasFatherFea-

ture and hasChildFeatures. The ﬁrst property indi-

cates that feature C is a subfeature of B, and we can

say that hasFatherFeature(C,B). The second property

indicates that B has the child feature C. The axiom

hasChildFeatures(B,C) represents it.

The context model has a ContextRoot, which rep-

resents the context variation in which applications

need to perform adaptations. ContextEntity is the ob-

ject or entity that is observed by the context in ques-

tion, for instance, a mobile device. Context informa-

tion indicates which information the system will read

since not all elements provided by the context entity

are relevant to context-aware applications. The bat-

tery level and the Wi-Fi signal strength are examples

of context information that can be captured by a mo-

bile device.

Figure 3 shows the composition rules and adap-

tation rules that compose a CASLP. They are repre-

sented by Composition Rule and Context Rule classes

respectively.

3.3 Veriﬁcation Rules of CAFM

The third process of our approach is the CAFM veriﬁ-

cation. An OWL ontology is the input of this process.

Semantic Enrichment and Veriﬁcation of Feature Models in DSPL

545

Figure 3: Fragment of Top ontology representing the Com-

position Rules and Adaptation Rules in a CASLP.

In this step, we veriﬁed if the ontology obeys a sub-

set of well-formedness and consistency rules, which

were proposed in (Marinho et al., 2012). To do this,

we represent these rules in SWRL.

An SWRL rule is composed of a body expression

that implicates a head expression. If the body expres-

sion is reached the head sentence is executed. We

mapped the composition and adaptation rules writ-

ting the SWRL body as an inconsistent state of model

(the state of ontology individuals that means a broken

rule). We created OWL classes and individual proper-

ties that represents each type of inconsistency to write

the head expressions. Thus, when an ontology indi-

vidual reaches an inconsistent state, it is highlighted

associated to corresponding OWL class error.

In the expression CompositionLiteral (?x) ∧

MandatoryFeature(?y) ∧

hasFeaturedElement (?x, ?y) → W FCR1 (?x),

the rule WFCR1 indicates which an action re-

lated to a mandatory feature is an exceptional

situation. In case this inconsistency occurs the

individual will be highlighted as a WFCR1 type.

The same meaning is applicable to the expres-

sion ActionLiteral (?x) ∧ MandatoryFeature (?y) ∧

hasFeaturedElement (?x, ?y) → W FAR1 (?x). If

a mandatory feature y is present in a System Ex-

pression on adaptation rules, it will be highlighted

as a WFAR1 individual. Therefore, it is possible

to identify inconsistencies in the ontology after a

reasoner analysis.

4 FixOnto TOOL

We implemented a tool to help domain expert with

the enrichment and veriﬁcation process. FixOnto tool

allows CAFM modeling and semantic enrichment,

mapping CAFM into OWL ﬁles, model veriﬁcation,

and semantic search. Figure 4 shows a screenshot of

tool.

Figure 4: Fragment of FixOnto

4.1 CAFM Modeling and Semantic

Enrichment

FixOnto is an extension of the tool proposed in (Costa

et al., 2015). The modeling editor on the original soft-

ware was the start point towards FixOnto implemen-

tation. We inherited the tool area to graphically model

CAFM represented by (1) and the pallet represented

by (2) in Figure 4. We added concepts to assist do-

main experts in the SPL semantic enrichment process.

The options “TestCase” and “UseCase” represented

by (3) in Figure 4 can be associated with features and

context situations, making the model a richer CAFM.

The FixOnto is an Eclipse-based tool, gener-

ated from UML-like models using Eclipse Modeling

Framework (EMF) and Eclipse Epsilon

. By the use

of Epsilon, mapped CAFM can work with Java, the

programming language used to code the others steps

in FixOnto.

4.2 Automatic Mapping

By using FixOnto, domain specialist needs only to

perform a right-click on feature modeling area, select

option “wizard” and select “FixOnto - Fea2Onto Ext”

to translate the model into OWL, such as represented

by (4) in Figure 4. This option will generate the ﬁle

ﬁxonto.owl, representing a rich CAFM that is ready

to be analyzed.

FixOnto CAFM transformation is inspired by

the Fea2Onto tool proposed in (Filho et al., 2012).

Fea2Onto steps are: (i) Java objects generation from

the feature model, and (ii) ontology generation from

objects, using OWL Java library and guided by a top

ontology. In the FixOnto, we coded a Fea2Onto ex-

tension, which is a new implementation that including

http://www.eclipse.org/epsilon/

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

546

context-awareness concepts.

4.3 Model Veriﬁcation and Semantic

To perform model veriﬁcation, the user should se-

lect option “CAFM Analyser”, represented by (5) in

Figure 4. The FixOnto searches for a ﬁle named

ﬁxonto.owl and performs an update including SWRL

rules contained in a ﬁle named rules.ﬁx. After update

ﬁxonto.owl, our tool calls the Pellet reasoner that in-

terprets and checks the SWRL rules. Among several

reasoners, we chose Pellet because of its rule support

(SWRL) and its free license (Dentler et al., 2011).

Figure 5: FixOnto broken rule message.

At last, after Pellet reasoning, FixOnto displays a

box containing found errors. In Figure 5, we can see

violated rules and OWL element to be veriﬁed.

Once ontology is enriched and valid, it is ready to

semantic search phase. Into FixOnto, domain experts

are able to make semantic searches using Prot

Query Syntax, and the OWL individuals are pre-

sented. Figure 6 shows an example of information

recovering (details in section 5.1).

5 EVALUATION

We modeled the CASPL Mobiline (Marinho et al.,

2013) into FixOnto to show beneﬁts of our approach

and examine its drawbacks. To control the results,

we selected only a part of Mobiline, represented in

Figure 1. The modeling example followed the steps

presented in the sequence of this paper.

5.1 Modeling a CASPL into the Tool

Firstly, we modeled the diagrams with FixOnto. Fig-

ure 1 placed in section 2.1 shows the System Model

and the Context Model used in this tool execution.

The expressions (1) and (2) in the section 2.1 are the

modeled rules. We included illustrative Test Cases

(TC1, TC2, and TC3) for increasing the CASPL ex-

pressivity.

After the initial modeling phase, FixOnto trans-

formed the CAFM in OWL-DL ontology. The gener-

ated ontology was stored in a ﬁle called ﬁxonto.owl.

Then, we veriﬁed the model correctness and con-

sistency by using well-formed rules. The modeled

CAFM did not present errors. We, then, inserted in-

consistencies into the model to check if error detec-

tion worked. Section 5.2 discusses in details the pro-

cess of fault insertions.

Finally, we performed a semantic search over the

ontology (ﬁxonto.owl), using DL Query. We selected

the option DLQuery Search in FixOnto. We queried

about test cases existent in the model. It may be im-

portant to developers verify the scope of a software

evolution. The queries and results can be viewed in

Figure 6.

5.2 Fault Insertion

We inserted errors corresponding to rules viewed in

Table 2 in section 2.3 and also extras rules. We

changed some axioms of ﬁxonto.owl ﬁle. In the

ﬁrst example, the original axiom was hasFeaturedEle-

ment(DisableMessage, ExchangeType.Synchronous).

The ExchangeType.Synchronous feature is an alter-

native feature. In this case, the rule WFAR1 in Table

2 is not broken.

For instance, in order to insert an error, we

changed the consequent of the context rule. We re-

moved the consequent ExchangeType.Synchronous

and, after, we included a mandatory feature (i.e.,

MessageExchange). The generated axiom after our

change was hasFeaturedElement(DisableMessage,

ExchangeType.Synchronous). Figure 5 shows the

tool result. It shows the broken rule.

Other implemented rule is represented

by expression hasFatherFeature(?x, ?y) ∧

hasFatherFeature (?y, ?x) → hasCycle(?x, ?y) ∧

W FSMR4 (?x). If a feature x is father of y and y is

father of x, it is said a cycle, and x is highlighted as

an individual of WFSMR4 error and the individuals

x and y are associated by the property hasCycle.

In this case, the original axiom was hasFatherFea-

ture(Privacy, Security). We changed the axiom to

hasFatherFeature(Security, Security), and the error

was thrown.

We implemented 12 rules and inserted 2 faults per

rule, resulting in 24 faults. The percentage of fault

detection for the implemented rules was 100%.

5.3 Discussion

This research proposes a solution for maximizing

reuse. We aim at helping domain experts to model and

Semantic Enrichment and Veriﬁcation of Feature Models in DSPL

547

Figure 6: FixOnto Query Search and Result.

retrieve information on CASPL development. The

proposed solution also includes model veriﬁcation,

helping to prevent modeling errors and reduce costs.

The tool in (Costa et al., 2015), Marinho et. al ap-

proach (Marinho et al., 2012) and semantic enrich-

ment approach (Filho et al., 2012) were the start point

of this proposal.

Regarding the ﬁrst research question (RQ1: How

to semantically enrich a DSPL and ensure that the

mapping remains correct after this association?), the

FixOnto provides an automatic model veriﬁcation the

can be performed after semantic enrichment. If the

veriﬁcation does not throw errors, we ensure that

mapping remains correct.

Concerning the second research question (RQ2:

How to make information retrieval and traceability

in DSPL easier?), we were able to trace the modeled

Test Cases, by using ”Query Search” FixOnto func-

tionality. A drawback is that domain specialist needs

to learn the DL Query syntax.

This research generated results that highlight the

beneﬁts of performing a semantic search over an ex-

pressive and valid CAFM ontology. The information

retrieval and traceability allow to the domain mod-

eler and other stakeholders take important decisions

along the development of context-aware applications.

However, some threats to validity were detected. We

categorized the threats to validity using the following

classiﬁcation proposed in (Wohlin et al., 2012): Con-

clusion, Internal, Construct and External. We iden-

tiﬁed threats to Conclusion and External validity as

follows.

Only one modeler used the FixOnto tool to ver-

ify its functionality implying in its threat to Conclu-

sion validity, which makes necessary to do an experi-

ment with a greater number of context-aware special-

ists. Moreover, we demonstrate the tool by using a

single academic CASPL, restricting the results to its

scope. This choice represents its threat to External va-

lidity. The modeling should be performed with more

CASPL to generalize the results.

6 RELATED WORK

The proposal in (Filho et al., 2012) provides a solution

to add semantic to SPLs. The authors use an ontology

to represent a feature model and use it as start points

to semantic enrichment. Narwane et al. (Narwane

et al., 2016) focus on formal modeling and analysis

of traceability in an SPL. Their work involves the re-

lation between features and core assets.

Regarding feature model veriﬁcation, Rincon et

al. (Rinc

on et al., 2014) propose a rule based ap-

proach using ontologies to analyse dead features and

false optional in feature diagrams. Their proposal

identiﬁes causes of inconsistencies and explaining

them in natural language. In (Zaid et al., 2009), au-

thors provide an ontology-based framework for fea-

ture modeling with a rule-based model to check con-

sistency and detect conﬂicts.

All these related work do not address contextual

aspects, which is the key point of our paper. In fact,

our approach is an extension of (Filho et al., 2012) re-

search. As previously discussed, the authors in (Mar-

inho et al., 2012) propose an approach to context-

aware model veriﬁcation and the authors in (Costa

et al., 2015) propose a tool that implements the ap-

proach. Thus, they address CAFM modeling, veri-

ﬁcation of feature model well-formedness and con-

sistency. However, unlike our work, their proposals

do not address semantic enrichment, which relates the

model to the core assets.

7 CONCLUSIONS AND FUTURE

WORK

Information retrieval and traceability help stakehold-

ers to take a decision during the development of

context-aware systems. Our research motivation has

been both the importance of the domain informa-

tion retrieval in CAFM and the importance of con-

sistency veriﬁcation of a DSPL (i.e. CASPL) during

the project initial steps. Early detection of modeling

inconsistencies allows time and money economy dur-

ing software development cycle. In the literature, it is

possible to ﬁnd solutions that verify traditional SPL

and DSPL, however, using these existing solutions,

the information in a feature model can still be insufﬁ-

cient. To fulﬁll this gap, we proposed an ontology-

based approach and a tool for adding semantic in

CASPL and for verifying its correctness and consis-

tency automatically.

For the proposition of our approach and respective

tool, we combine some existing solutions. For seman-

tic enrichment, the approach proposed by (Filho et al.,

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

548

2012) was one start point for our work. For automatic

veriﬁcation in CASPL, we used the framework pro-

posed in (Zaid et al., 2009), which suggests the imple-

mentation of rules in SWRL. The rules into FixOnto

were then implemented in SWRL following the rules

proposed by (Marinho et al., 2012).

We demonstrated the semantic enrichment and

automatic veriﬁcation of CAFM with a tool called

FixOnto, which is an extension of Fixture (Costa

et al., 2015). We also demonstrated the traceability,

which is very important to prevent errors and provide

time efﬁciency, using the model with DL Query.

There are evidences that our approach can be used

for traditional FM, but we still need to evaluate that in

future work as well as we aim to automatize the whole

approach. A usability evaluation should also be per-

formed over FixOnto to verify the beneﬁts to the ﬁnal

user, speciﬁcally the domain specialist. It is also im-

portant to model a second CASPL into the tool to an-

alyze different scenarios. We also aim to increase the

tool usability, displaying only human readable mes-

sages and excluding OWL individuals information.

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