A Framework for Generating Domain-specific Rule for Process

Model Customisation

Neel Mani

, Markus Helfert

and Claus Pahl

ADAPT Centre for Digital Content Technology, Dublin City University, School of Computing, Dublin, Ireland

Free University of Bozen-Bolzano, Free University of Bozen-Bolzano, Bolzano, Italy

Keywords: Domain Model, Domain-Specific Rule, Rule Language, Rule Generation, Business Process Model,

Variability Model, Model Driven Architecture, Model Transformation, Domain-Specific Language.

Abstract: The domain-specific model-driven development requires effective and flexible techniques for implementing

domain-specific rule generators. In this paper, we present a framework for rule generation through model

translation with feature model, a high-level of the domain model to translate into low-level of rule language

based on the paradigm of software reuse in terms of customisation and configuration with domain-specific

rule strategies benefit mode-to-text translations. This framework is domain-specific where non-technical

domain user can customise and configure the business process models. These compositions support two

dimensional of translation modularity by using software product line engineering. The domain engineering is

achieved by designing the domain and process model as a requirement space, it is also called template model,

connecting with feature model through weaving model. The feature model is a high-level input model to

customise the template model to an implementation. The application engineering is achieved by supporting

the rule definition and configuring the generated rules. We discuss the development approach of the

framework in a domain-specific environment; we present a case study in a Digital Content Technology (DCT)

domain.

1 INTRODUCTION

Nowadays the reuse of software is one of challenges

to implement to customise the application based on

the end-user requirements. The traditionally model-

driven engineering use for extracting the knowledge

from a high-level design model to low-level langua-

ge. How to effectively fill the gap between the softw-

are product line engineering and the models to extract

the knowledge from high-level models to low-level

language. The primary requirement of the framework

where end user can opt their requirement to adapt the

desirable models. For achieving the desirable model,

there are several steps to require the implementation

of configuration-based systems where translate the

high-level design to low level execution. Enterprises,

usually have high level legacy models and they are

domain and process based models. Automatic code

generation (Edwards, Brun et al. 2012, Prout, Atlee et

al. 2012, Ringert, Roth et al. 2015) is a well-known

approach for getting the execution code of a system

from a given abstract model. Rule is an extended

version of code that requires compiling and building

it in a configurable mode. Rule generation is an

approach by which the higher-level design model as

input and the lower level of execution code as output

are shaped. It may be platform independent or

platform specific (Bergmayr and Wimmer 2013)

approach. In Model-Driven Architecture (MDA), the

techniques are expressed by design models as the

primary artefact of development and use them as a

basis for obtaining a configurable domain-specific

rule for business process model customisation in

different ways (Gonçalves 2015). But it does not talk

about the variability of models (domain model and

process model).

The major motivations is to address the automated

customise the models based on the end-user and then

generate the domain-specific rule(DSR) (Mani,

Helfert et al. 2016) from high-level complex domain

models at run time. The domain models are

characterised by the complexity of their structures as

specified in the metamodel or the non-trivial nature

of constraints imposed on them or a combination of

these two factors. It is a problem by which the

existing fully-automated model generators fail.

Mani N., Helfert M. and Pahl C.

A Framework for Generating Domain-speciﬁc Rule for Process Model Customisation.

DOI: 10.5220/0006512201630171

In Proceedings of the International Conference on Computer-Human Interaction Research and Applications (CHIRA 2017), pages 163-171

ISBN: 978-989-758-267-7

Our contribution is a framework for customising

and configuring process model in digital content

technology domain in dynamic environment. The

production capability is based on reusable model as

core assets that configure the constraints of the

domain problems by non-technical domain user. At

runtime, we propose the model-based rule generation

for process models that use the variability models.

They capture the user requirement to process

automatically the rule generation and configuration of

the process model’s composition. Mass customisation

updates are possible through software Product Line

Engineering (SPLE), which provides a platform to

domain expert for designing and developing the

domain template (domain model and process model).

The paper is organised as follows. We compare

the related work Section 2. Section 3 is the motivation

of research paper. In Section 4, we discuss the overall

proposed approach, in which we discuss DSLs,

feature model, domain model and the rule. Section 5

introduces and describe the design of the framework

and its component and In Section 6, we evaluate the

overall approach in terms of usability The Section 7

concludes the paper reflecting the future work.

2 RELATED WORKED

In this section, we review the present concept,

technologies and techniques that motivate us to

present the relevant problem under domain constraint

study. It includes a short description of automatic

code generation, language and applications based on

Model-driven principles.

Our research work uses the SPLE life cycle for

customising the feature based on the user requirement

and MDA framework that uses in generating the do-

main-specific rule of the customised domain model.

The domain expert performing on the high level

of abstraction, the machine works low level of

language (Gupta 2015). The code generation

technique accelerates the transformation process

between designing the application and its implement-

tation in terms of executable code (Prout, Atlee et al.

2012, Gurunule and Nashipudimath 2015). The code

generation is an approach for converting the high

level of design constructs into low level of executable

code constructs. The automatic code generation is

converting software design into executable code

without little bit intervention of programmer or

developer (Gurunule and Nashipudimath 2015). The

generation technique reduces the effort of the manual

writing of programming code, avoiding the manual

programming errors like syntactical (spelling

mistake) and programmatically semantical errors.

The web application development is description

of processes, techniques and selecting appropriate

models for web engineering. The web engineering

uses automatic web application methodologies such

as UWE (Koch, Knapp et al. 2008), WebML (Ceri,

Fraternali et al. 2000) and WebDSL (Groenewegen,

Hemel et al. 2008) approaches. The design and

development of Web applications and tools provide

mainly conceptual models (Ceri, Fraternali et al.

2002), focusing on content, navigation and presenta-

tion models (Linaje, Preciado et al. 2007, Moreno,

Meliá et al. 2008). Now , the model driven approach

for dynamic web application, based on MVC and

server is described by Distante et al. (Distante,

Pedone et al. 2007). However, these methods do not

consider as the user requirement of the variability mo-

del. To simplify our description, we have considered

the user requirement and according to the need of the

user, the user selects the feature and customises the

enterprise application in the dynamic environment.

Currently, there is no such type of methodology

or process of development for creating a rule-based

system in a web application (semantic). Dioufet et al.

(Diouf, Maabout et al. 2007, Musumbu, Diouf et al.

2010) propose a process which merges UML models

and OWL ontologies for business rule generation.

The solution for semantic web is ontologies, UML

and applying the MDA approach for generating or

extraction rules from high level of models. Although,

the proposed combination of UML and semantic

ontologies are for extracting the set of rules in target

rule engine, but they only generate the first level of

the abstraction of the rules.

We have proposed a classification of several

quality and governance constraints elsewhere (Pahl

and Mani 2014): authorisation, accountability,

workflow governance and quality. The domain-

specific rule language (DSRL) (Mani and Pahl 2015)

is a combination of rules and BPMN. Our approach

provides a framework architecture for generating the

domain-specific rule and configuring the generated

rule for process model customisation using the

variability model.

3 MOTIVATIONS

The enterprise needs to change and adapt

progressively the dynamic behaviour at runtime in

response to changing conditions, updating new

features in support of enterprise and business

applications and in the surrounding business process

models. Nowadays, the requirements for developing

Figure 1: Framework for SPLE: domain and application engineering, problem and solution space (Mani, Helfert et al. 2017).

and customisation process are increasing. However,

the process model languages (Standard , Reichert and

Dadam 1998, White 2004, Van der Aalst and Ter

Hofstede 2005, List and Korherr 2006) restrict

domain user and designer to describe explicitly the

process execution plan as pre-defined task control

flow, data flow and work/process allocation schema.

The changes reflect at modelling stage or design

phase which make the process model rigid

(Boukhebouze, Amghar et al. 2011, Gromoff,

Kazantsev et al. 2012, Rangiha and Karakostas 2013).

Thus, adaptability is emerging as crucial and an

underlying capability in highly dynamic

environment.

During business process customisation and

adaptation in dynamic and domain-specific environ-

ments, the end users have only domain knowledge

without technically customising and adapting the

changes and configuration is done in process models

at runtime. For example, process activities and sub

process could be activated and deactivated (removed)

(Mani, Helfert et al. 2016) based on user requirement

at application engineering level.

4 SOFTWARE PRODUCT LINE

ENGINEERING

The key contribution is the use of a feature model to

bridge the gap between an assumed domain model

(here in ontology form) and the domain-specific rule

extension of a business process. The feature model

streamlines the constraints customisation for business

processes. It acts as a bridge between the domain

model and the rule language.

We propose to abstractly support the different

SPLE aspects from MDA. The MDA aims at

capturing all the relevant aspects of the framework

through appropriate models. The stakeholders’

motives are more prominently captured by models

than the implementation codes. Models capture the

requirements or the intentions of the end users more

effectively, help to avoiding accidental implementa-

tion details and also are more suited for analysis.

Models, in MDA context, are much more than being

supportive artifacts; rather, these are actually source

artifacts which can be utilized for automated analysis

and/or rule generation.

In our approach, the aim is the solve the need for

expressive and easy-to-understand adaptation.

Therefore, the domain model is implemented as a

variability model which describes the variants in

which the domain expert designs the domain template

composition. Since the domain model abstracts the

rule generation, the definition of a bridge between the

elements in the variability model and the elements in

the domain model could be used to support the

dynamic rule generation in the underlying domain-

specific environment.

To this end, we use a weaving model as an

additional software asset input to project the changes

in the features of the variability model, on abstract

elements in a domain model. In other words, the

weaving model works as a bridge between the

elements in these models.

The goal of Software Product Lines Engineering

(SPLE) is developing a set of software components

and systems with similar characteristics and catering

to the requirements of a domain through management

of certain features (Kang, Cohen et al. 1990). SPLE

effectively tunes down the development cost and

Table 1: Weaving model.

Variablity Model Feature Model Process Model Domain Model

1. V1 F1 Task1 Class1

2. V2 F2 Task2 Class 2

3. V3 F3 Task3 Class 3

Figure 2: Models that support the SPL for autonomic rule generation.

market time of the software, enhances the overhead

quality and engineering by reusing assets strategically

within the domain. SPLE uses adopted techniques to

manage reusability with commonality and variability

model that effectively categorizes the common assets

and their variabilities. The software product line

framework has two phases (see Figure 2): (i) the

Problem Space for describing the problem

description, the type of applications, or an individual

application in the category; (ii) Solution Space for

providing the software components to solve that

problem; iii) Domain engineering phase may be

defined as a formally represented platform in which

development and implementation products take

place. In SPLE, the variability modeling technique

known as “Feature Models” are used for the purpose

of portraying variability in hierarchical manner

differentiation or simplification of features in

hierarchy of products belonging to a software family.

The DSRs are products, based on activate or

deactivate features in the feature model to manage the

requirement of domain user or stack holder during

feature selection. Therefore DSRs depend upon the

feature of the feature model. The rule generation in

the variability model produces the adaptation space

with 1) and all possible constraint configurations of

the process model (in terms of active and inactive

features in the feature model with parametric value)

and 2), customising the process model in terms of the

functional and operational use. In order to avoid the

problem or an interruption (error, system halt,

malfunctioning, wrong interpretation, etc.) during

rule generation and or configuration in critical service

application, we argue that the feature model and its

possible configurations should be validated and

verified at the runtime.

The SPLE and MDA are not only complementary,

but their integration may lead to significant gains in

various applications. On one side MDA provides for

abstractly representing various aspects of a product

line, while on the other SPLE provides for a well-

defined application scope. This provides a sound

basis for the development and selection of appropriate

modeling languages. Further, the automated genera-

tion of system configurations is made possible by

accurate models as a result of automated analysis and

rule. MDA provides effective techniques for

conveying the results of specifying variability as

follows:

 Metamodeling: It refers to type of systems that

express, for specific domains, having the

constraints that are associated with a product

line, with key abstract syntax characteristics and

static semantic constraints

Domain-specific languages (DSLs): In order to

formalize the specifics of structure of the

product line, its behavior and requirements with

respect to domain, the DSLs provide notations

governed by extendable metamodels.

 Model transformations and rule generators: It

refers to ensuring the consistency of

implementations of the product line along with

the corresponding analysis. The analysis may be

retrospect to functional and QoS requirements.

Key advantages of using MDA in conjunction

with variability of SPLs are (1) rigorously capturing

the commonalities and variabilities in a family of

systems and (2) helping automated repetitive tasks

that must be accomplished for each product instance.

Figure 2 shows how to combine modelling and

model transformations to develop an SPLE-MDA for

Digital Content Technology. First, the assets of the

SPLE-MDA model elements are described with a

family of Digital Content Technology. These model

elements are conformed to the metamodel (of Domain

Model), which is a DSL for Digital Content

Technology. Second, the decision model is another

model which specifies the aspects or characteristics

(named features) of a particular Digital Content

Technology. Third, a weaving model projects the

features on the DSL for scoping the domain model.

Finally, the output system is obtained through a

model (scoped DSRL) to text (domain-specific rule)

the translation.

5 DESIGNS OF FRAMEWORK

The dynamic process model adaptation is possible by

customising and configuring the overall architecture

of the framework at runtime through predefine

domain template (domain model and process model)

models. This approach works on principal of active-

tion and deactivation of models. However, the

proposed framework follows strategy for the dynamic

activation and deactivation of features in the domain

template, based on the end-user requirement. After,

the domain template is modeled and the weaving

model has connected the feature model and the

domain template (via a virtual relational table) at

design time, the customisation and adaptation may

take place at run time. The customisation of the

process model and rule generation achieve at runtime.

This approach works under the domain-specific

environment.

An overview of our approach, enabling

customisation and configuration of the component

connections for the rule is shown in Figure 3. As

illustrated, the approach covers both dynamic process

model adaptation and development compositions.

The aim is to design appropriate architecture modes

of the feature selection based on user requirements

and generate the configurable rule for process model

customisation. At run time, the users configure the

Figure 3: Detailed Framework of Rule Generation and Process Model Customisation.

Figure 4: Feature model of Machine translation.

rule, thereby facilitating the framework to quality

configuration in terms of effective, efficient and

satisfactory usability. The feature model

requirements are continually analysed and validated

every feature by target feature validator with respect

to the runtime feature section. It reflects in the

template model environment as an activation and

deactivation through weaving model. In the case of

the feature selection violation, the target feature

validator gets prompted and validated accordingly on

basic criteria of the feature model. For example, the

user can remove the mandatory feature from the

system. In the following, we briefly discuss each

phase and describe their actives.

The framework offers a dynamic solution for rule

generation and process model customisation. The

framework consists of two sections: Development

Composition and Dynamic Process Adaptation. Dev-

elopment composition consist of different elements

such Weaving Model, Process model, metamodel,

DSRL configuration generator. We have developed

two building blocks to provide utilisation of the

domain model variability and community at run time:

(1) the domain model customiser uses a weaving

model (table 1). The weaving models are models

which capture different kind of relationships between

models (Del Fabro and Valduriez 2007). It is about

finding a similar type of transformation patterns

between model elements integrating transformations

in the tabular form.

The weaving helps to execute the customisation

process base on validate feature. The validate feature

is a set of analysed and verified features, which is

captured by the requirements of a domain user or

stakeholder. (2) In the Rule generator, the models and

metamodel that created in the domain model

customiser are used in the MDA at different level M1

and M2 (Figure 3). The generated rule configuration

has two different types: per-configured (at design

time) and post-configurable (dynamic) metamodel

In the Dynamic Process Adaptation, the proposed

structure carries out the following steps to support

dynamics adaptation. First, the model-based

configurator collects the information from various

models. If the requirement of target feature is

violated, the feature selection or customisation

activities such as: rename, move, update and delete

feature, affect the activation and deactivation process.

The execute customisation models pass the captured

requirement to weaving model for further activities.

The customised and generated sets of rules are output

from the Development Composition.

We propose an approach that offers a solution for

the dynamic adaptation of rule generation and, BPM

customisation and configuration composition. The

variability model services are responsible for carrying

mandatory or optional feature of the BPM and

metamodel. The feature of a process can be activated

or deactivated at any moment of time, i.e. design or

run time. We introduce a mechanism where domain

expert and user can perform their tasks in a simply

way. A domain expert can design high-level of

solution for domain, based on that solution, domain

user can modify and customise model elements

(activities) in any process over time.

The model elements activation and deactivation

depend on the variability model and what are the

requirement of end user. In this research, there are

two different groups of users, involving one expert in

domain with modelling knowledge and other have a

functional domain knowledge, but they are non-

technical. Therefore, we select software product line

engineering (SPLE) platform where the user can

perform the tasks. The SPLE is a standard model to

develop software applications using platform and

mass customisation (Böckle, van der Linden et al.

2005).

5.1 Feature Model

Figure 4 shows the feature model used in our case

study. For instance, the Language Model, Transfer

based MT, Interlingua MT, Direct MT and

Translation Model features are variants that can be

used during execution to accomplish the machine

translation functionality in the Model point.

5.2 Weaving Model

A product line feature model represents variabilities

and commonalities. The features in a feature model

are simply symbols with their type. Mapping features

to other models (feature model, domain model and

process model,) expressed in Table 1. Next, we show

how to perform the mapping by means of a weaving

model (Geyer and Becker 2002). We use a static

weaving model for managing the variability

relationships among all models. The principle

argument for using the static weaving model is with

domain-specific environment, when the domain

experts have significant domain knowledge. They

design and develop the domain template at design

(static) time. This weaving approach enables us for

scoping and configuring the Domain Models from a

set of given Features.

6 EVALUATIONS

We evaluate the DCT, analysing the time and

efficiency of its configuration including satisfaction

and operational compliance based on computing

human interaction by the end user. The goal of the

analysis is to get a comparative analysis of the time

and efficiency of configured DCT and its sub-process

systems. The emphasis is on analysing the relative

benefit of the proposed framework and a manual or

traditional or baseline approach regarding the

efficiency, effectiveness, and satisfaction with

function and operational compliance support. The

feature selection and configuration scenarios involve

to modifications resulting from improvement of the

complex process activity that affected the function

and operation of the process.

The current example is a part of a digital content

processing process model as a sample process for the

rule composition of business processes and domain

constraints that conduct this process. The source text

is translated into target language by the machine

translation activity. The translated text quality

decides whether further post-editing activity is

required. Usually, these constraints are domain-

specific, e.g., referring to domain objects, their

properties and respective rules.

The capability of the solution is effective,

efficient and satisfactory by the user when used under

specified conditions. We evaluate our contribution in

usability and forcing it on the following aspects

which is illustrated in Figure 5.

There are different component in the ISO standard

9241, applying the specification of usability into both

hardware and software designs. We discuss the

usability criteria and its goals.

In this overall framework, the usability criteria define

as effectiveness, efficiency and satisfaction. It

specified that end-users achieve specified goals are in

domain-specific environments.

Figure 5: Usability evaluation criteria.

 Efficiency. The comparison between the time

taken of configuring domain constraints in

manual and semi-automatic process, based on

that find which process is more efficient.

 Effectiveness. The generated rule configuration

in terms of accuracy to prevent or protect errors

to achieved the configuration of domain

constraints goal.

 Satisfaction. The measure of end-user’s

comfort and acceptability of the overall

framework.

7 CONCLUSION AND FUTURE

WORK

In this paper, we have presented a model-based

framework, generating the domain-specific rule for

process model configuration and customisation in

dynamic environment. A case study on digital content

technology shows the applicability of this framework,

a realisation development approach and a proof of

concept prototype validating the feasibility of the

proposed approach. The main benefit of the

framework has: 1) Non-technical domain experts can

customise and configure the business process without

knowing any technical knowledge;2) The framework

can configure the domain constraints in dynamic

environment; and 3) A third is to evaluate the

framework architecture mechanisms in terms of

usability. It can be handled the completeness of

configuration of rule in terms: effectiveness,

efficiency and satisfaction base on computer human

interface by end use. The completeness of rule

configuration means performance, error free rule,

syntactically and semantically correctness after

configuring the rule.

We plan to extend this approach in combination

with our existing work on business process model

customisation based on user requirement (feature

model, domain model and process models), so that a

complete development life cycle for the customisati-

on and configuration of business process models is

supported. We also see the need for further research

that focuses on how to define the DSRL in terms of

abstract and concreate syntactical definition with

grammar formation across different domains and how

to convert conceptual models into generic domain-

specific rule language which are applicable to other

domains. So far this is a model to text translation, but

shall be improved with a system that learns from

existing rules and domain models, driven by the

feature model approach with automatic constraints

configuration, and to result in an automated DSRL

generation.

ACKNOWLEDGEMENTS

This research is supported by Science Foundation

Ireland (SFI) as a part of the ADAPT Centre at Dublin

City University (Grant No: 12/CE/I2267).

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