=Paper=
{{Paper
|id=Vol-1164/DoctoralConsortium01
|storemode=property
|title=A Traceability-based Method to Support Conceptual Model Evolution
|pdfUrl=https://ceur-ws.org/Vol-1164/DoctoralConsortium01.pdf
|volume=Vol-1164
|dblpUrl=https://dblp.org/rec/conf/caise/Ruiz14
}}
==A Traceability-based Method to Support Conceptual Model Evolution==
https://ceur-ws.org/Vol-1164/DoctoralConsortium01.pdf
A Traceability-based Method to Support Conceptual
Model Evolution
Marcela Ruiz
PROS Research Centre, Universitat Politècnica de València, Spain
lruiz@pros.upv.es
Abstract. Renewing software systems is one of the most cost-effective ways to
protect software investment, which saves time, money and ensures uninter-
rupted access to technical support and product upgrades. There are several mo-
tivations to promote investment and scientific effort for specifying systems by
means of conceptual models and supporting its evolution. As an example, the
software engineering community is addressing solutions for supporting model
traceability, continuous improvement of business process, organisational reen-
gineering, information system maintenance, etc. Model-driven techniques have
been developed in order to analyse systems raising the abstraction level of its
specification. However, a support for conceptual model evolution by means of
model-driven techniques is still needed. This thesis proposes a traceability-
based method that involves model-driven capabilities for designing and provid-
ing guidelines, techniques, and tools to support conceptual model evolution.
The main idea is to support information system analysts in the tasks related to:
justify why the conceptual models have evolved, report and specify what ele-
ments have evolved, and guide how to carry out evolution in certain predefined
organisational contexts. We plan to apply our method to guide the evolution of
an E-shopping software. This way, we also provide mechanism to facilitate in-
dustrial adoption.
Keywords: conceptual model evolution, reengineering frameworks, traceabil-
ity-based support, business process modelling, intentional modelling, pattern
definition, delta analysis
1 Introduction
Software maintenance and information system evolution are activities that receive
significant dedication by industry. This is one of the reasons that motivate the infor-
mation systems engineering community to investigate in this area. Organisations are
aware on the need to apply mechanisms and strategies in order to encompass proc-
esses and products in changing environments. For instance, in organisational context,
companies need to rethink business processes, infrastructures, technologies, re-
sources, etc. according to new demands from their environment or changes in their
organisational objectives. Business processes should also be transformed to support
the new processes and tasks that result from the involvement of new objectives or
goals in the organisation. Then, constant organisational change and its influence in
processes and products must be considered as a fundamental rule of competitive strat-
egy for continuous improvement [1]. For software systems, the high pressure of a
very short time-to-market often forces developers to implement the code of the appli-
cation directly, without using a disciplined development process, which may have
disastrous effects on the quality and documentation of the delivered software applica-
tion [2]. These practices have been the motivation for opening new research lines in
order to support post-delivery life-cycle activities. Besides, with regard to the keynote
of the ERCIM News 88 magazine1, some of external drivers for changing software
are innovation, cost reduction and regulation; factors that need to be supported by
techniques, tools and methods.
The main goal of my PhD thesis is to design a traceability-based method that involves
model-driven capabilities in order to support conceptual model evolution. The main
idea is to provide a model-driven method that can be used by information system
analysts in order to provide them with reports and evidences to help decision making
in information system evolution contexts. This paper summarizes the author’s PhD
work and project, working for two years and a half, under the supervision of Dr. Ser-
gio España Cubillo in the PROS Research Centre of the Universitat Politècnica de
València.
2 Problem Description and Research Methodology
Traditionally in software system development, the evolution process and information
system maintenance have been faced by means of the reengineering process, change
specification, evolution metrics, goal-driven requirements engineering and model
management. For these reason, we explore current solutions in these fields in order to
find related research that confronts conceptual model evolution.
The reengineering process is commonly defined and widely used by the scientific
community by means of the metaphor of the “horseshoe” model, which purpose is to
present the reengineering process in a figure (the horseshoe is basically a left-hand
side, a right-hand side and a bridge between the sides). In general terms, the left-hand
side of the horseshoe model consists of an extraction from an existing system to get
the system specification, the right-hand side consist of conventional software devel-
opment activities, and the bridge between the sides consists of a set of transformations
from the old system to the new one [4]. Both, the left-hand side and right-hand side
represent different levels of abstraction of the system. Nowadays, the Object Man-
agement Group (OMG) is working on promote an industrial consensus on modernisa-
1
The ERCIM News 88 special theme was “Evolving Software” 3. Visser, J., Change is the
constant, in ERCIM news - Special theme: Evolving Software. 2012: Sophia Antipolis
Cedex, France. p. 3.. The magazine put together a set of papers to give an overview of both
traditional and emerging software engineering techniques, tools and approaches used by
software evolution experts.
tion of existing application by means of the initiative named Architecture-Driven
Modernisation (ADM) [5]. This initiative is based on the MDD paradigm to automate
the horseshoe model. However, full support for the evolution process (the bridge be-
tween the sides) is still missing. The authors of [6] aimed to automate the horseshoe
model, although it is not severely applied.
Goal-driven requirements engineering approaches faced goal modelling from differ-
ent perspectives of use. Some of those uses are: understanding the current organisa-
tional situations and need for change, decision making, relating business goals to
functional and non-functional system components and validation of compliance be-
tween system specification and stakeholders’ goals [7]. Co-evolution approaches has
been proposed in order to understand reciprocal evolution of system components [8].
Nevertheless goal specification related with change models and specification of evo-
lution grains is still an open research field.
System change and stability analysis in order to derive or facilitate system evolution
is confronted by [9]. A method to support the elicitation of evolution requirements
and a generic syntax to specify them is explored in [10]. Also, metrics for classifying
and measuring software evolution are analysed by [11]. Even though, specification of
evolution in with formal conceptual models and measurement techniques to provide
meaningful to kick start analysis is still needed.
Model management confront problems in many databases application domains (e.g.
data warehousing, semantic query processing, meta-data management, meta-data
integration, schema evolution etc.); research projects in this area are aiming at provid-
ing high-level abstractions artefacts in order to offer a generic solution [12-13]. Bern-
stein [14] presents a full description of all of the model management operators. More-
over, no complete frameworks to support enterprise information system evolution
have been proposed yet.
The problems detected establish the motivations in which this PhD thesis is founded.
2.1 Research Questions Objectives and Means
We follow design science to classify our research questions in knowledge problems
(KP) and practical problems (PP) [15]. This way, we are looking for highlighting our
research results by means of producing useful artefacts. This thesis is focused on con-
ceptual model evolution. To achieve the main goal, we conceive the following re-
search questions:
• RQ1 (KP). What elements are common in conceptual model evolution? The an-
swer to this question should clarify terminology, stakeholders, and helps to estab-
lish a conceptual framework to facilitate reasoning about conceptual model evolu-
tion.
• RQ2 (KP). Which are the current conceptual model evolution methods? The an-
swer to this question should establish the state of the art about current conceptual
model evolution support.
o RQ2.1 (KP). Which of these methods are model-driven oriented?
• RQ3 (PP). How can be supported a conceptual model evolution method? The an-
swer to this question refers to the main goal of this thesis.
o RQ3.1 (PP). What guidelines are needed in order to evolve conceptual mod-
els?
o RQ3.2 (PP). What techniques are needed in order to facilitate the use of the
method?
o RQ3.3 (PP). What tools are needed in order to support the use of guidelines
and techniques?
• RQ4 (PP). How can possible scenarios be integrated in the conceptual model evo-
lution method? The answer to this question refers the modules to support business
process evolution, goal-driven evolution, and reengineering.
• RQ5 (KP). How can the model-driven method to support conceptual model evolu-
tion be validated? The answer to this question should establish a validation frame-
work to measure feasibility, trade-off and sensitivity.
Means
To achieve the main goal and solve the research questions, three main means are con-
ceived: a) Expert views. My directors are experts to guide my decisions to provide
solutions of the addressed problem. b) Technological support. We are expert in
model-driven tools as Eclipse. This way, we have capabilities to provide tool support
for the method. c) Collaboration with other research groups. Collaboration increases
our perspectives to provide solutions. d) Action research. Our proposal is motivated
by the needs of real information system analysts.
3 Research Methodology
This PhD project follows the design science framework to design a new artefact: a
model-driven method to support conceptual model evolution. The research methodol-
ogy is explained by means of regulative cycles that were conceived in order to answer
the research questions. Fig. 1 presents the research methodology.
Fig. 1. Overview of the research methodology
Since our proposal focus on the development of a new artefact, the main cycle of the
research methodology is an engineering cycle (EC1. Design a model driven method to
support data system evolution). Concretely, this cycle is formed by 5 main tasks: T1)
problem investigation; T2) solution design; T3) solution validation; T4) solution im-
plementation; and T5) implementation validation.
An information system needs evolve. Since the information system is specified by
means of models, we investigate current research to support conceptual model evolu-
tion. We identify the stakeholders or possible users of the method. To define the prob-
lem and define the method, we provide a conceptual framework to avoid terminology
incoherence. In addition, we establish the criteria to judge the solution success when
we finish the engineering cycle. These activities are related to T1.
In T2 we explore available solutions by reviewing state of the art. We design a new
solution; i.e. our method. To do that, we design the guidelines of use; we provide
techniques to facilitate the use of the method; and we develop tools (prototypes built
in the laboratory) to support guidelines and techniques. Also, we design the support
for the modules of business process evolution, goal-driven evolution and reengineer-
ing frameworks.
The method is validated in T3. We demonstrate the feasibility by means of
lab-demo. We establish a comparative with the results of the lab-demo with the crite-
ria defined in T1.3. Also, we evaluate trade-off and sensitivity of the solution.
In T4 we implement the method using Eclipse based tools, design an action re-
search protocol to transfer the solution to be used in practice. Finally, in T5 we assess
the operability of the tool, stakeholder’s satisfaction and criteria of success by means
the results of the action research protocol carried out in T4.
4 Proposal
We face the design of the method by two main motivations: 1) Market pull or demand
pull and 2) Technology push [16]. The first one refers our motivation to evolve the E-
Shopping software (a real case and we have into account the user needs). We call it
market-driven solution. The second one refers our motivation to provide an invention
without proper consideration of whether or not it satisfies a set of specific user needs.
We call it technology push-driven solution.
To design the method, we have been inspired by the metaphor of a “horseshoe” of
Kazman et. al. [4]. Carrying the horseshoe metaphor to the MDD field, an interesting
evolution method can be provided for different scenarios. As a result, models are the
main artefact and the analysis of them is in a high level of abstraction. The traceabil-
ity-based support plays the main role in the method; it provides two types of traces:
Vertical traces to relate elements that specify different characteristics of information
systems (e.g., processes, goals, etc.); and horizontal traces are accounted to relate
evolution of elements.
To use the method, the analyst should carry out the four tasks presented in the Fig. 2:
1. Define evolution question, in this task the analyst decides what characteristic of
evolution process want to know. The analyst follows a set of guidelines in order to
know if s/he wants to obtain information about justifying why the conceptual models
have evolved, reporting or specifying what have evolved, or analysing how to evolve
conceptual models according to a set of predefined solutions for certain contexts.
2. Specify As-Is and To-Be models, in this task the analyst specify the current and
desired system to be analysed applying the evolution modules.
3. Apply evolution modules, in this task the analyst applies the module that corre-
sponds with s/he evolution question.
Fig. 2. Overview of the traceability-based Method to support conceptual model evolution
For the why question (3.1 Why: Goal-driven analysis), a goal driven model evolu-
tion support is provided. The vertical traceability is established between two informa-
tion system specification languages. As a proof of concept, we have aligned the i*
framework with the Communication Analysis modelling techniques. Goal models are
connected with delta models that specify changes in the information system.
For the what question (3.2 What: Delta-based analysis), a set of metrics are pro-
vided in order to report meaningful information about the evolution processes, ele-
ments involved and the conceptual impact of changes.
For the how question (3.3 How: Pattern-based guidelines), a set of patterns to
evolve business process models have been established. The patterns are connected
with delta models to register what changes implies the application of patterns.
4. Obtain reports and evolution models, in this task the analyst obtain the results of
modules application. Based on the results the analyst can provide meaningful infor-
mation about conceptual model evolution processes and make decisions based on
evidences.
The method is in continuous improvement and re-adjusts. The modules have been
designed; the implementation has being developed in Eclipse-based tools.
5 Progress of the Thesis
In 2012, organisational reengineering frameworks have been studied, focusing on
RQ1 and RQ2. Furthermore, the alignment between the process and the goal perspec-
tives were explored. As a proof of concept, we have aligned the i* framework with
the Communication Analysis modelling techniques. This proof of concept refers the
RQ4. Also, we implemented the alignment of this modelling languages in an Eclipse-
based tool (this implementation refers RQ3.). And we analysed the benefits and the
limitations of aligning process and goal perspectives. We started a first version of the
definition of the artefacts to support model evolution (Traceability support).
In 2013, the modules of the method were designed and reported. We carried out an
experimental task with master students to analyse vertical traceability between con-
ceptual models.
In 2014-2015 we plan to establish the method guidelines and delta analysis tech-
nique formalisation. In addition, we are looking for implementing pattern definition
metamodel and evolution metamodel in an Eclipse plug-in (RQ3).We plan to validate
the method and the prototype by means of laboratory demos. The idea is to estimate
scalability, trade-off and sensitivity of our method. This validation refers RQ5.
We plan to finalize the implementation and the implementation validation of the
method in 2015.
Acknowledgments
I acknowledge to my supervisor Sergio España for his invaluable support and advices
to drive my thesis and encourage my research career.
This PhD project has been supported by the Spanish Generalitat Valenciana ORCA
(PROMETEO/2009/015); the FPI grant of the Universitat Politècnica de València
(3146); the European Commission FP7 Project CaaS (611351); and the ERDF struc-
tural funds.
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