=Paper=
{{Paper
|id=Vol-3727/paper1
|storemode=property
|title=Research directions for agile model-driven engineering
|pdfUrl=https://ceur-ws.org/Vol-3727/paper1.pdf
|volume=Vol-3727
|authors=Kevin Lano,Hessa Alfraihi,H. Haughton
|dblpUrl=https://dblp.org/rec/conf/staf/LanoAH24
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==Research directions for agile model-driven engineering==
https://ceur-ws.org/Vol-3727/paper1.pdf
Research directions for agile model-driven
engineering
Kevin Lano1 , H. Alfraihi2 and H. Haughton3
1
King’s College London, Strand, London, UK
2
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
3
Holistic Risk Solutions Ltd, London, UK
Abstract
Model-driven Engineering (MDE) advocates software development based on the use of software models
and model transformations. Despite extensive research, and many cases of MDE application across a
range of application domains, MDE still remains a minority practice among software developers. Agile
MDE aims to combine MDE techniques and processes with those of agile methods such as Scrum, and has
achieved significant adoption within certain industries, such as automotive systems and telecoms. Based
on recent empirical studies and on a survey of 349 papers over the last 25 years of Agile MDE research,
we highlight key issues with the current use of MDE, and identify where Agile MDE approaches can
contribute to advance MDE practice.
Keywords
Model-driven Engineering, Agile Development, Systematic Literature Review
1. Introduction
The ideas behind model-driven engineering (MDE) originated in the 1990’s, from work on the
integration of object-oriented modelling and formal methods, and were principally motivated
by the aim of increasing the rigour of object-oriented development [1, 2, 3]. The concepts of
MDE were explicitly defined in the Model-driven architecture (MDA) from the OMG, which
emphasised modelling at different levels of abstraction, together with model transformations
(MT) to support automated mapping between models, and from models to code1 . The MDE field
has substantially expanded and developed over the last 25 years, however concerns continue to
be raised regarding the high resources and skills required to utilise MDE for industrial software
development [4, 5, 6]. Section 2 summarises current MDE practice and issues with the use of
MDE.
Agile methods have been combined with MDE in ‘Agile MDE’ approaches, which aim to
gain benefits for agile and/or MDE development approaches by adopting techniques from both
methods. Here we provide an updated survey of the agile MDE field, which has developed
AMDE 2024: Agile Model-driven Engineering Workshop, Part of the Software Technologies: Applications and
Foundations (STAF) federated conferences, Eds. S. Tehrani, H. Alfraihi, S. Rahimi and J. Troya, 8–12 July 2024, Enschede,
Netherlands.
" kevin.lano@kcl.ac.uk (K. Lano); haalafraihi@pnu.edu.sa (H. Alfraihi); h.haughton@gmail.com (H. Haughton)
0000-0002-9706-1410 (K. Lano)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
1
www.omg.org/mda/specs.htm
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
significantly since the survey of [7]. In Section 3 we employ the systematic literature review
(SLR) procedure of [8], and in Section 4 use the survey results to propose specific research
directions whereby Agile MDE concepts can contribute to improve MDE practice and adoption.
2. Current state of MDE practice
In this section we analyse the current situation regarding MDE adoption and practice, and iden-
tify issues which have been highlighted by empirical research and raised by MDE practitioners
in forums at recent conferences.
We consider the following MDE survey papers from the last 7 years: [4, 5, 6]. Table 1
summarises the findings and recommendations of these papers.
The findings of these surveys about MDE benefits and issues are generally consistent: it is
agreed that MDE can have significant benefits in terms of software quality and development
productivity and rigour. However, gaining these benefits requires various obstacles to be
overcome, particularly due to the effort and specialised skills needed to effectively use MDE
languages and tools. There is a lack of appropriate or usable tools, and poor integration between
MDE tools and between MDE processes and mainstream software engineering practices.
The above findings are also consistent with the results of more specialised surveys, such
as [9, 10, 11] and [12, 13]. In [11], a large-scale interview study was conducted to identify the
benefits/disadvantages of model transformation languages (MTLs) compared to general-purpose
languages (GPLs) for writing model transformations. While positive benefits of using MTLs
in terms of productivity of transformation development, and for conciseness and clarity of
transformations were described, these were often counterbalanced by the need for specialised
skills and tools, and weaknesses of MTLs in areas such as transformation reuse, learnability
and poor tool support. GPLs were perceived to generally provide much stronger tooling,
interoperability and ecosystem support compared to MTLs.
From the above surveys it can be seen that the potential benefits of MDE are widely recognised,
but have been limited by various factors – of which Poor MDE process integration; Lack of
appropriate tools (especially for requirements analysis and maintenance); Poor usability and high
skill requirements of MDE languages and tools have been repeatedly mentioned by multiple
empirical studies.
Subsequent to the survey and roadmap of [6], the landscape of software engineering and the
specific MDE field have changed in some significant ways:
• The SysML 2.0 standard has been published2 . Driven by industry concerns about the
deficiencies of UML and SysML, this is a substantial revision of SysML which emphasises
traceability and textual modelling.
• The digital twins field has emerged as a means of applying modelling concepts for the
simulation of real-world situations and artefacts in health, urban environments and many
other application areas [14, 15, 16].
• Cloud-based and web-based applications have become a principal means for supporting
software development and deployment. These applications include low-code development
platforms [17].
2
github.com/Systems-Modeling/SysML-v2-Release/
Table 1
Summary of MDE surveys
Survey MDE benefits MDE issues Suggested solutions
Abrahao et al Abstraction; Poor UX. Move to user-driven MDE.
[5] Automatic Lack of MDE integration Improved
(2017) code (within MDE & between MDE interoperability &
generation. and other development practices). integration.
High skills needed to Improve/evaluate
use MDE processes/tools. UX to reduce cognitive
load of tools.
Complex languages & First class support for
tools. customisation &
domain-specific
modelling.
Access to MDE skills.
Bucchiarone Abstraction; Legacy models; Improved MDE agility
et al, [6] Automation. Uncertainty in design; and maintenance
(2020) Dealing with software support.
change.
Marginal adoption of Improved support
MTL by industry. for bidirectionality.
Challenges of IoT, Domain-specific MDE.
smart systems.
Complexity of graphical Increase adoption of
modelling tools; textual modelling.
Linking different views.
Scalability; Use of AI to support
Accidental complexity more powerful MDE
of MDE. tools.
Limited end-user Deeper use of example-
tailoring capabilities based modelling.
of MDE.
Education issues. Model repositories;
Improved MDE teaching.
Alfraihi & Improved Manual processes used More flexible &
Lano [4] software for requirements analysis & customisable tools/
(2023) quality; model creation; notations.
Consistency of Lack of tool support
models & code; for requirements specification,
Improved testing, operations, deployment
communications or maintenance.
across teams; Lack of skills; Emphasis on
Improved Steep learning curve; learnability & integration;
reusability. Lack of integration. User evaluation
for MDE tools.
• Perhaps most significant, the field of large language models (LLMs) [18] within machine
learning (ML) has emerged as a radical advance on previous ML capabilities, and there is
a rapid and ongoing growth in the application of LLMs to SE and to MDE [19, 20].
3. Research in agile MDE
Agile MDE denotes the combination of agile methods [21] and MDE. Agile MDE approaches
were proposed soon after the inception of MDE, and have been used in a wide range of industrial
settings. To examine the evolution of Agile MDE, we carried out a systematic literature review
(SLR) of publications in the Agile MDE field, using the standard SLR procedure of [8]. The
research questions we aimed to answer from the survey were:
RQ1: How has the Agile MDE field evolved and developed over time? In particular:
1. Have there been changes in the application areas considered?
2. Have there been changes in the agile or MDE techniques used?
3. Have there been changes in the industrial versus academic orientation of papers?
RQ2: Have any ‘best practices’ for Agile MDE emerged?
We performed a search on the three leading academic online libraries: Scopus, IEEE Xplore
and ACM Digital Library, using the search criteria:
1. Peer-reviewed conference or journal papers.
2. Must include both ‘agile’ and ‘model-driven’ in the abstract.
3. In the date range 1999–2024.
The search generated 1407 candidate papers divided as follows: Scopus – 350, IEEE Xplore –
80, ACM Digital Library – 977. All survey data is at: zenodo.org/records/11611376.
After removal of duplicate papers, and inspection of abstracts and contents to exclude
irrelevant publications, the total number of articles was reduced to 349 unique relevant papers.
Table 2 summarises the highlights of the data analysis of these papers in terms of RQ1 (1)
and (2), and Figure 1 gives a chart of the number of publications in the field per year3 . This
shows a generally increasing trend in the number of publications.
With regard to RQ1 (3), there is a relatively high proportion of ‘industrial’ papers in the
survey results, that is, papers with at least one industry-based co-author and/or including a
real-world case. Overall, 38% of the papers (131) can be classified as industrial in this sense, and
this percentage has increased from 30% in the period 1999–2010 to 41% in 2011–2020 and 40%
in 2021–2024.
With regard to RQ2, there has been extensive research on techniques which could be used to
align MDE and agile processes, such as rapid and evolutionary prototyping [22, 23], model and
code co-evolution [24], ‘low code’ development [25, 26, 27], increased customer involvement
in development [28, 29, 30, 31], and the automated generation of models from code or from
natural language [32, 33], with a particular focus on the generation of models from user stories
[34, 35, 36, 37]. These techniques can all potentially benefit MDE practice by accelerating
development steps and reducing the manual effort required to create and manage models.
3
Only complete years with at least one publication are included.
Table 2
RQ1: Agile MDE publication characteristics
Period Papers Academic/ Main application Technical
Industrial areas Areas
1999–2010 84 55/24 Mechatronics/CPS 17 Code generation 10
Web/SOA 15 Lightweight MDE 10
Software Eng. 10 Architecture 9
Business/finance 5 Testing 7
2011–2020 211 124/86 Software Eng. 43 Requirements Eng. 21
CPS/Embedded 34 DSLs 18
Web/SOA 32 Testing/TDD 15
Business/finance 30 Code generation 13
2021–2024 54 31/21 Software Eng. 15 Requirements Eng. 6
Business/finance 12 Lightweight MDE 5
CPS/Embedded 10 DevOps/CI 4
Figure 1: RQ1: Agile MDE papers published per year
4. Research directions for MDE improvement
The analysis of Section 2 identified problems with MDE adoption in three categories:
• MDE process issues: e.g., lack of integration with SE practices.
• MDE language issues: language complexity and difficulty in learning the languages.
• MDE tool issues: complex tool interfaces, poor usability, lack of customisability of tools.
Taking into account the above analysis, and the work in the field of agile MDE described in
Section 3, we can point out specific research directions which have the potential to improve the
uptake and effectiveness of MDE:
• Simplified and lightweight MDE languages, particularly the use of textual modelling
languages instead of graphical, and transformations specified in terms of concrete syntax
instead of metamodel features.
• Usability improvement, by means of user co-design of MDE languages and tools, and
formal usability analysis of produced tools/languages.
• Simplified and lightweight tools, using a ‘low-code’ style of interaction and accessible via
web or mobile UIs.
• Process and tool use support for users, including requirements formalisation and applica-
tions of machine learning for modelling guidance and transformation construction from
examples.
• Improved integration with SE, in areas such as reverse-engineering/legacy code manage-
ment (model-driven reverse engineering, MDRE), software architecture, CI/CD/DevOps
and open-source/collaborative development platforms.
These areas are summarised in Figure 2.
Figure 2: MDE roadmap directions
4.1. Simplified and lightweight MDE languages
The complexity of graphical modelling languages such as UML have often been cited as an
obstacle to software modelling use. Large graphical models can require significant effort to
comprehend or manage, and text-based models are often more convenient for operations such
as model management, model merging, and for model comparison using tools such as diff , etc
[13]. There is some empirical evidence that the effort and time needed for understanding textual
models is lower than for graphical [38, 39]. Thus further research on text-based modelling
approaches and tools is a potentially useful research direction.
Model creation is a critical activity in MDE, which currently requires high effort and expertise.
Model creation effort could be reduced by (i) using requirements-to-model synthesis approaches
(requirements formalisation) based on natural language processing or machine learning [40]; (ii)
reverse-engineering models from existing code using MDRE [41, 42]; (iii) using ‘sketch-to-model’
techniques to produce a formal model from informal sketches, analogous to ‘sketch-to-code’
tools4 .
4
www.microsoft.com/en-us/ai/ai-lab-sketch2code
With regard to DSL tooling, the use of metamodelling and model transformations relying on
metamodels may complicate and increase the effort of DSL support. Instead, a more lightweight
text- and grammar-based approach using tools such as ANTLR has been recommended [43, 44]
and should be further investigated. Transformations could be specified based on the concrete
syntax of source and target languages, using a text-to-text MTL such as 𝒞𝒮𝒯 ℒ [45], thus
avoiding the need for transformation developers to know the metamodels of the languages.
4.2. User-centered languages and tools
MDE languages and tools should be designed with respect to the needs and capabilities of
potential users, and evaluated with respect to such user needs. Ideally, experts from a domain,
representative of end-users in that domain, should play a leading/directing role in the develop-
ment of MDE language and tool support for that domain. Thus, agile methods concepts of an
‘on-site customer representative’ should be adopted [46]. An example of this process was the
creation of a DSL, MathOCL, for the specification of mathematical models in finance [47]. This
work was initiated and guided by a domain expert, working together with MDE experts.
MDE tool and language creators should consider and evaluate the cognitive loading of their
products, and avoid factors which increase this load, such as novel or non-standard concepts
and syntax. Aligning tools and languages to existing user skills and knowledge can help to
overcome obstacles to use.
4.3. Machine learning and MDE
An area with rapidly increasing research activity is the application of AI and machine learning
(ML) to software engineering [20]. Within MDE, ML has been used to (potentially) improve the
user experience by simplifying and automating MDE processes such as the construction of mod-
els from informal requirements [48, 40, 49, 50], and the construction of model transformations
from examples [51, 45]. ML has also been used for advisory systems to assist practitioners to
analyse and improve models, and to support model-based DevOps processes [52]. In principle
an LLM with sufficiently wide-ranging knowledge could be used as an ‘operating system’ or
platform able to provide specialised knowledge-based services, including software modelling
services, after suitable fine-tuning [19]. However there can be reliability and accuracy issues
with non-symbolic machine learning techniques such as LLMs, which means that their outputs
need to be considered as the views of a fallible and sometimes inconsistent expert, rather than
being used to directly automate processes [53, 54, 55]. Current LLMs also have inadequate
modelling knowledge to be directly used to automatically support MDE activities [48, 40, 49].
Thus a human modeller needs to inspect, correct and validate the models produced by an LLM.
We consider that an important research direction to pursue is the creation of new LLMs pos-
sessing sufficient code, natural language and modelling knowledge in order to more effectively
support MDE activities such as model creation from requirements, model review, and model
quality improvement. In turn, this work will require the creation and curation of substantial
model datasets to support training of the LLMs.
Further investigation should be carried out into the use of symbolic ML for learning precise
software engineering tasks, such as model transformation (MT) and code generation and
abstraction [56, 45, 41].
4.4. Simplified and lightweight tools
Low-code software development approaches are techniques whereby end-users can construct
applications with a minimum of traditional coding. They typically utilise a pre-existing set
of services (such as data persistence services) provided by a cloud-based platform. There is
some overlap between low-code and MDE approaches [17], because MDE approaches usually
involve the replacement of explicit coding by more abstract/platform-independent specification
and design. But many low-code approaches do not involve construction of models, instead
drag-and-drop facilities as in interface builders such as Xcode are used. In addition, while a goal
of low-code is to reduce the development effort and skills needed to produce an application,
MDE approaches instead move effort from code construction to model construction, and require
advanced skills. Our view is that the low-code approach would be useful for MDE tools that
aim to support high user configurability, or to enable users with lower MDE technical skills to
utilise MDE techniques. In this guise they would actually be ‘low modelling and low code’ tools,
enabling the construction of model-based solutions using pre-existing components such as OCL
libraries for file management and networking [57]. ML support could be used to automate the
selection of model elements and design and architecture choices from user input provided in
natural language. Speech input/output could be used for improved interactivity.
4.5. Integration with software engineering practices
Surveys have emphasised the need for MDE techniques to be seamlessly integrated with existing
development practices such as continuous integration/deployment and DevOps [5, 4]. The
selective use of MDE techniques to enhance agile development processes has been a major
theme in the Agile MDE field (Section 3), with modelling being used to facilitate communication
between heterogeneous teams [13], and to accelerate the production of prototypes [22, 23]. A
major challenge in MDE and mainstream SE integration is how to combine automated code
generation with manually-produced code, including legacy code. Prospective solutions include
model and code co-evolution [24] and automated extraction of models from code [42].
The use of MDE with open-source projects involving collaborative development is also an
area where more work is needed [33]. The study of [58] showed that MDE use may have positive
benefits in the early life of such projects, but this effect diminishes over time, which may be
due to the general lack of effort to synchronise models and code, so that the models become
progressively more out-of-date over time as project contributors make code changes.
5. Conclusions
This paper has distilled the analysis of recent surveys of the MDE field, and highlighted signifi-
cant work from the field of Agile MDE in order to identify key topics for research to improve
MDE usability and the acceptance of MDE by general software practitioners.
In summary, we identified research focus areas of simpler and more lightweight MDE lan-
guages, tools and processes, with increased user involvement in MDE language and tool defini-
tion and evaluation, and increased automation support for MDE processes, using ML or other
techniques. Improved integration with SE processes is also needed. We consider that these
research directions have potential to expand the use of MDE and to increase the benefits from
such use, and hence ultimately to improve the practice of software engineering.
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