=Paper=
{{Paper
|id=Vol-2405/13_paper
|storemode=property
|title=Lowcomote: Training the Next Generation of Experts in Scalable Low-Code Engineering Platforms
|pdfUrl=https://ceur-ws.org/Vol-2405/13_paper.pdf
|volume=Vol-2405
|authors=Massimo Tisi,Jean-Marie Mottu,Dimitrios S. Kolovos,Juan de Lara,Esther Guerra,Davide Di Ruscio,Alfonso Pierantonio,Manuel Wimmer
|dblpUrl=https://dblp.org/rec/conf/staf/TisiMKLGRPW19
}}
==Lowcomote: Training the Next Generation of Experts in Scalable Low-Code Engineering Platforms==
https://ceur-ws.org/Vol-2405/13_paper.pdf
Lowcomote:
Training the Next Generation of Experts
in Scalable Low-Code Engineering Platforms
Massimo Tisi1 , Jean-Marie Mottu1 , Dimitrios S. Kolovos2 , Juan de Lara3 ,
Esther Guerra3 , Davide Di Ruscio4 , Alfonso Pierantonio4 , and
Manuel Wimmer5
1
LS2N, IMT Atlantique, Université de Nantes, France
2
University of York Helsington, UK
3
Universidad Autónoma de Madrid, Spain
4
Università degli studi dell’Aquila, Italy
5
JKU Linz, Austria
{massimo.tisi, jean-marie.mottu}@imt-atlantique.fr,
dimitris.kolovos@york.ac.uk, {juan.delara, esther.guerra}@uam.es,
{davide.diruscio,alfonso.pierantoni}@univaq.it, manuel.wimmer@jku.at
Abstract. Low-Code Development Platforms (LCDPs) are software de-
velopment platforms on the Cloud, provided through a Platform-as-a-
Service model, which allow users to build completely operational appli-
cations by interacting through dynamic graphical user interfaces, visual
diagrams and declarative languages.
Lowcomote will train a generation of experts that will upgrade the cur-
rent trend of LCDP to a new paradigm, Low-Code Engineering Platform.
This will be achieved by injecting in LCDPs the theoretical and techni-
cal framework defined by recent research in Model Driven Engineering,
augmented with Cloud Computing and Machine Learning techniques.
1 Project Identity
– Project acronym: Lowcomote
– Project title: Training the Next Generation of Experts in Scalable Low-
Code Engineering Platforms
– Partners: Institut Mines Telecom (Coordinator), University of York, Uni-
versidad Autónoma de Madrid, University of L’Aquila, Johannes Kepler Uni-
versität Linz, British Telecom, Intecs Solutions, Uground, CLMS, IncQuery
Labs, The Open Group, Metadev, SparxSystems, Amazon Web Services
– Website: www.lowcomote.eu
– Funding: EU H2020-MSCA-ITN-2018 - Innovative Training Networks (ITN)
– Project start date/duration: 1st January, 2019 (48 months)
2 Introduction
A large number of research efforts in the history of computer science have shared
a common objective: enabling the construction of software applications without
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
74 M. Tisi et al.
recurring to traditional procedural computer programming. Early products of
these research efforts, like fourth-generation programming languages and rapid
application development tools, have seen a significant industrial adoption in al-
ternating periods since the nineties, but never reached a dominating position in
the software construction landscape. Today a new generation of tools is success-
fully addressing this challenge in specific domains. They are commonly called
Low-Code Development Platforms (LCDPs): software development plat-
forms on the Cloud, provided through a Platform-as-a-Service (PaaS) model,
that allow users to build fully operational applications by interacting through
dynamic graphical user interfaces, visual diagrams and declarative languages.
In a recent report [1], the research firm Forrester is forecasting a market in-
crease for LCDP companies to over $21 billion over the next four years. Major
PaaS players are all integrating LCDPs (e.g., Google App Maker and Microsoft
PowerApps [2]) in their general-purpose solutions.
LCDPs build on top of recent advancements in visual programming, auto-
matic code generation, and Cloud infrastructures. LCDPs provide the user with
a completely managed environment for the whole application life-cycle, including
development, deployment, execution and monitoring. Therefore, they are highly
usable by customers with no particular background in programming, called cit-
izen developers in the LCDP jargon, without sacrificing the productivity of
professional developers. So far LCDPs have been especially successful for the
development of domain-specific applications in four market segments, identi-
fied in [3]: database applications, mobile applications, process applications, and
request-handling applications. Internet of Things (IoT) will be the fifth one.
Lowcomote considers three main limitations that hamper the use of LCDPs.
Scalability: as LCDPs are currently popular for the development of small ap-
plications but their use in large-scale and mission-critical enterprise applications
is requested for next evolution. Fragmentation: Each tool vendor proposes his
own low-code development paradigm, associated with a particular programming
model. Software-only systems: while citizen developers have little knowledge
of programming, they are often experts in some other engineering domain. These
domain experts expect to be able to use their knowledge in the application, at
the right level of abstraction and using familiar formalisms.
Lowcomote is an Innovative Train- Model-Driven Engineering
ing Network (ITN) aiming to train a
Machine Learning Cloud Computing
generation of professionals in the de-
sign, development and operation of Low-Code Low-Code
new LCDPs, overcoming the current Development Engineering
limitations, by being scalable (i.e.,
supporting the development of large- Fig. 1: Lowcomote in a nutshell
scale applications, and using artefacts coming from a large number of users),
open (i.e., based on interoperable and exchangeable programming models and
standards), and heterogeneous (i.e., able to integrate with models coming from
different engineering disciplines). They will drive the upgrade of the current land-
scape of LCDPs to Low-Code Engineering Platforms (LCEPs).
Lowcomote: ITN on Scalable Low-Code Engineering Platforms 75
The 15 doctoral subjects (https://www.lowcomote.eu/call/) in Lowco-
mote address the hard research problems that are blocking factors for the achieve-
ment of these objectives. One of the key research ideas of Lowcomote is that
platforms for Low-Code Engineering (LCE) can be built by injecting in LCDPs
the theoretical and technical framework defined by recent research in Model-
Driven Engineering (MDE). MDE research promotes a linguistic approach to
software engineering, by the design of domain-specific modelling languages and
their automated transformation in executable artefacts. Research in MDE has
produced modelling frameworks supported by open standards, with a natural
support for heterogeneity. While scalability has been traditionally problem-
atic in MDE, some Lowcomote partners have been involved in the EC FP7 re-
search project MONDO (2013-2016), which made substantial breakthroughs
on scalable modelling and model management. Lowcomote builds on top of
MONDO theoretical and technical solutions, but upgrades them to enable their
use for LCEPs. This is achieved by a multi-disciplinary research program inte-
grating research on Cloud Computing and Machine Learning.
3 Main challenges and outlines
This section identifies the main challenges that need to be overcome, and outlines
the envisioned contributions of Lowcomote.
3.1 Low-code Engineering of Large-Scale Heterogeneous Systems
LCDPs are typically targeted to users with low technical profile. The supported
applications are frequently form-based enterprise systems (sometimes deployed
on mobile phones), which are described graphically. However, the approach fails
to scale for more complex software – beyond simple CRUD applications – and is
not applicable to domains with high impact nowadays, like data science or IoT.
Shortening the development times in those domains, by being able to apply a
low-code development approach, would be have a considerable economic impact
in today’s industry.
To scale LCDP for more complex applications and challenging domains, Low-
comote proposes employing solid language engineering principles [4] to generate
a new generation of scalable Cloud-based low-code editors. By using abstraction
and graph summarisation techniques, such editors will be able to scale to models
exceeding millions of elements. At the same time, the platform will be neutral on
the underlying modelling technology to avoid vendor lock-in issues, and support
heterogeneity (required by e.g., the IoT domain) through multi-view modelling.
To help users with low technical profile to develop complex applications, we pro-
pose enriching LCDPs with recommendation chatbots. These will interact using
natural language and use information retrieval and machine learning techniques
to recommend appropriate task completions, or propose example fragments and
templates.
76 M. Tisi et al.
Based on the notion of Active DSL [5], LCEP will support citizen devel-
opment of sophisticated mobile apps, beyond the capacities of current LCDPs.
Our targeted apps will be collaborative, and offer interaction based on graphical
diagramming, where elements may be geolocated on maps. They will be able to
incorporate information from open APIs to access services (e.g., weather), and
be context sensitive, able to adapt to changing conditions like device position,
time or other conditions retrieved from APIs.
Finally, LCEP will be applied to construct domain-specific LCDPs for data
science and IoT. Many tasks of data science projects are currently performed in
an ad-hoc way, and solutions many times need to be re-implemented with more
scalable and robust technologies before entering in production. Our envisioned
LCDP will provide the necessary languages for a high level description of the
solution, supporting integrated deployment automation, and scalable execution
and monitoring. Similarly, our envisioned IoT platform will enable the scalable
modelling of heterogeneous, dynamic data sources and devices, while retaining
the low code benefits of reduced development times.
3.2 Large-scale Repository and Services for Low-Code Engineering
Over the last decade, several technologies have been proposed for supporting a
wide range of model management activities, such as model validation, transfor-
mation and code generation. Even though existing technologies provide prac-
titioners with facilities that can simplify and automate many steps of MDE
processes, empirical studies show that some barriers still exist for their wider
adoption [6]. In particular, the support for discovery and reuse of existing mod-
elling artefacts is very limited. As a result, similar transformations and other
model management tools often need to be developed from scratch, thus raising
the upfront investment and compromising the productivity benefits of model-
based processes. Lowcomote will advance the state of the art by developing
an extensible model repository specifically defined to address issues related to
scalability and heterogeneity of the considered modelling artefacts. This model
repository will be used to enable capability discovery and reuse across different
low-code systems using reinforcement learning algorithms.
Another important aspect of model repositories is which kind of information
may be stored and which kind of tools, systems, and applications may contribute
content [7,8]. Currently, model repositories are limited for mostly hosting differ-
ent versions of design models created by development environments [7]. But
further support is needed, especially for runtime aspects and runtime informa-
tion may be manifold [9]. For instance, runtime information may concern the
interactions with the development environment and the associated repository
which will be explored as interaction mining. Another important kind of run-
time information which should be accessible in model repositories is operational
data monitored in running systems which may lead to more reactive models
usable not only for design but also for supervisory control and data acquisition.
Finally the repository will include a quality workbench (QW). The latter
requires to distribute the tests (which are low-code artefacts) and to query the
Lowcomote: ITN on Scalable Low-Code Engineering Platforms 77
repository to get and to store operational data of the tests, considering scalabil-
ity [3]. Moreover, the QW will manage the test automation [10] by implementing
low-code tests based on existing but distributed low-code artefacts and testing
the distribution over the Cloud itself [11].
3.3 Scalable Low-Code Artefact Management
Management and evolution of large-scale LCEPs presents complexity and scal-
ability challenges. Even though MDE provides a number of tools for artefact
management, current MDE frameworks fall short when it comes to manipulating
and analysing models of the required complexity and scale, both in terms of per-
formance and in terms of resource requirements (e.g., memory footprint) [12,13].
Moreover, currently-available tools for developing and executing model transfor-
mations struggle with very large and distributed models over a parallel and dis-
tributed environment [14]. The challenge is multiplied in the case of live model
transformations, which are continuously run in the background and react to
changes and events in the environment [15]. This requires investigating an ef-
ficient search-based pattern matching algorithm that can apply to models of
millions of elements, and the solution must efficiently run on distributed and
parallel environments.
Lowcomote will go beyond the state of the art by developing the theoretical
underpinnings and technical components for a model management engine that
is able to execute transformations over a highly distributed computing infras-
tructure, providing scalability both in terms of model size and transformation
complexity, and reacting immediately to changes in the environment. The engine
will support multiple distributed programming models, automatically selecting
the most convenient one, for the whole transformation or only part of it. The
engine will support also chains of transformations, with mechanisms for the au-
tomatic selection and composition of model transformations in the Cloud.
Lowcomote will advance the state of the art in the field of efficient persistence
and querying of large-scale models. Novel techniques and algorithms will be
developed to optimise computationally-expensive queries operating on models
specified with different modelling languages and in model representation formats.
Advanced mechanisms for selective/partial loading and persistence of large-scale
models will be proposed and implemented.
4 Conclusion
In this paper we have provided an outline of the main challenges of Low-Code En-
gineering and how the Lowcomote ITN will address them. The technical outcome
will be a set of components of a single open platform named Lowcomotive. It
will be a platform as a service, based on open standards at all levels (e.g., EMF,
language-server-protocol, OpenStack). A frontend will provide LCE languages
and smart interfaces that end-users will exploit to produce their artefacts. A
backend will come in the form of a LCE repository providing server-side services
78 M. Tisi et al.
for LCE developers. Industrial partners will provide case studies to ESRs develop
and run their experiments.
Acknowlegments. This project has received funding from the
EU Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie grant agreement No 813884.
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