Starting June 2022, our book “Model Driven Software Engineering in Practice” (co-authored with Jordi Cabot and Manuel Wimmer) is now also available via Springer . This means the price is actually lower, and if you are affiliated with an academic institution, you may even have free access to the book through your institutional access. Check it here.
Together with the book, we provided free bonus material including: over 500 slides on MDE ready to use for classes; and all the book examples in the book’s github repository.
Modern User Interfaces (UIs) are becoming complex software artifacts themselves, through integration of AI-enhanced software components that enable even more natural interactions, including the possibility to use Natural Language Processing (NLP) via chatbots or voicebots (aka., Conversational User Interfaces or CUIs).
Some times, several types of UIs are combined as part of the same application (e.g. a chatbot in a web page), what it is known as Multiexperience User Interface. These multiexperience UIs may be built together by using a Multiexperience Development Platform (MXDP).
“Multiexperience development involves ensuring a consistent user experience across web, mobile, wearable, conversational and immersive touchpoints”. [Gartner]
A typical scenario of multiexperience user interaction could unroll as follows (see image below too). Suppose that a customer on a Sunday morning wants to buy a new technical product (a cell phone or a home theater system). He first interacts with his home assistant (like Alexa or Google assistant) to ask it to find the best nearby tech store open on Sunday. With this information in mind, he looks at the store web site on his PC and, being satisfied with the kind of store, he asks the web site chatbot to find the type of products he is looking for. After browsing the various alternatives, he finds one item he likes, and sets the place and the product as preferences on his mobile phone. He reads the details of the product on the phone while walking to his car. When he reaches the car, he transfers the information about the place to the car navigation system and drives there. Finally, in the stores he looks around, tries various items, reads the reviews about them on a dedicated mobile app, and finally picks up the product and pays for it.
This kind of dynamic and seamless interaction demands a variety of complex design and implementation mechanisms to be put in place. Clearly, also very critical integration, evolution, and maintenance challenges need to be faced for these CUIs. Developers need to handle the coordination of the cognitive services to build multiexperience UIs, integrate them with external services, and worry about extensibility, scalability, and maintenance.
We believe a model-driven approach for MXDP could be an important first step towards facilitating the specification of rich UIs able to coordinate and collaborate to provide the best experience for end-users. Indeed, most non-trivial systems adhere to some kind of model-based philosophy, where software design models (including GUI models) are transformed into the production code the system executes at run-time. This transformation can be (semi)automated in some cases.
Our recent research tackles the application of model-driven techniques to the development of software applications embedding a multiexperience UI.
The research has been published in our recent paper Towards a Model-Driven Approach for Multiexperience AI-based User Interfaces, co-authored by Elena Planas, Gwendal Daniel, Marco Brambilla and Jordi Cabot, recently published in the International Journal on Software and Systems Modeling (SoSyM) available online here (open access).
The paper contribution is twofold:
we raise the abstraction level used in the definition of this new kind of conversational and smart interfaces.
we show how these CUI models can be used in conjunction with more “traditional” GUI models to combine the benefits of all these different types of interfaces in a multiexperience development project.
In practice, we propose a new Domain Specific Language (DSL), that generalizes the one defined by the Xatkit model to cover all types of CUIs, and we show how this seamlessly integrates with appropriate extensions of the IFML model to design comprehensive multi-experience interfaces.
IFML model integrating traditional navigation of a web interface and a chatbot component.
You can refer to the full paper here for covering the details. The paper reference is:
Planas, E., Daniel, G., Brambilla, M., Cabot, J. Towards a model-driven approach for multiexperience AI-based user interfaces. Software and System Modeling (SoSyM)20, 997–1009 (2021). https://doi.org/10.1007/s10270-021-00904-y
In the context of Domain-Specific Modeling Language (DSML) development, the involvement of end-users is crucial to assure that the resulting language satisfies their needs.
In our paper presented at SLE 2017 in Vancouver, Canada, on October 24th within the SPLASH Conference context, we discuss how crowdsourcing tasks can exploited to assist in domain-specific language definition processes. This is in line with the vision towards cognification of model-driven engineering.
The slides are available on slideshare:
Indeed, crowdsourcing has emerged as a novel paradigm where humans are employed to perform computational and information collection tasks. In language design, by relying on the crowd, it is possible to show an early version of the language to a wider spectrum of users, thus increasing the validation scope and eventually promoting its acceptance and adoption.
Ready to accept improper use of your tools?
We propose a systematic (and automatic) method for creating crowdsourcing campaigns aimed at refining the graphical notation of DSMLs. The method defines a set of steps to identify, create and order the questions for the crowd. As a result, developers are provided with a set of notation choices that best fit end-users’ needs. We also report on an experiment validating the approach.
Improving the quality of the language notation may improve dramatically acceptance and adoption, as well as the way people use your notation and the associated tools.
Essentially, our idea is to spawn to the crowd a bunch of questions regarding the concrete syntax of visual modeling languages, and collect opinions. Based on different strategies, we generate an optimal notation and then we check how good it is.
In the paper we also validate the approach and experiment it in a practical use case, namely studying some variations over the BPMN modeling language.
The full paper can be found here: https://dl.acm.org/citation.cfm?doid=3136014.3136033. The paper is titled: “Better Call the Crowd: Using Crowdsourcing to Shape the Notation of Domain-Specific Languages” and was co-authored by Marco Brambilla, Jordi Cabot, Javier Luis Cánovas Izquierdo, and Andrea Mauri.
You can also access the Web version on Jordi Cabot blog.
The artifacts described in this paper are also referenced on findresearch.org, namely referring to the following materials:
Long time ago, in the past century, the International DB Research Community used to meet for assessing new research directions, starting the meetings with 2-minutes gong showsto tell each one’s opinion and influencing follow-up discussion. Bruce Lindsay from IBM had just been quoted for his message:
There are 3 important things in data management: performance, performance, performance.
Stefano Ceri had a chance to speak out immediately after and to give a syntactically similar but semantically orthogonal message:
There are 3 important things in data management: modeling, modeling, modeling.
Data management is continuously evolving for serving the needs of an increasingly connected society. New challenges apply not only to systems and technology, but also to the models and abstractions for capturing new application requirements.
In our retrospective paper, we describe several models and abstractions which have been progressively designed to capture new forms of data-centered interactions in the last twenty five years – a period of huge changes due to the spreading of web-based applications and the increasingly relevant role of social interactions.
We initially focus on Web-based applications for individuals, then discuss applications among enterprises, and this is all about WebML and IFML; then we discuss how these applications may include rankings which are computed using services or using crowds, and this is related to our work on crowdsourcing (liquid query and crowdsearcher tool); we conclude with hints to a recent research discussing how social sources can be used for capturing emerging knowledge (the social knowledge extractor perspective and tooling).
It’s also true that model-driven engineering is not necessarily the tool of choice for this to happen. Why? As technician, we always tend to blame the customer for not understanding our product. But maybe we should look into ourselves and the kind of tools (conceptual and technical) the MDE community is offering. I’m pretty sure we could find plenty of space for improvement.
Jordi Cabot, Robert Clarisó, Marco Brambilla and Sébastien Gerard submitted a visionary paper on Cognifying Model-driven Software Development to the workshop GrandMDE (Grand Challenges in Modeling) co-located with STAF 2017 in Margburg (Germany) on July 17, 2017. The paper advocates for the cross-domain fertilization of disciplines such as machine learning and artificial intelligence, behavioural analytics, social studies, cognitive science, crowdsourcing and many more, in order to help model-driven software development.But actually, what is cognification?
Cognification is the application of knowledge to boost the performance and impact of any process.
The thesis of our paper is that cognification will also revolution in the way software is built. In particular, we discuss the opportunities and challenges of cognifying Model-Driven Software Engineering (MDSE or MDE) tasks.
MDE has seen limited adoption in the software development industry, probably because the perception from developers’ and managers’ perspective is that its benefits do not outweigh its costs.
We believe cognification could drastically improve the benefits and reduce the costs of adopting MDSE, and thus boost its adoption.
At the practical level, cognification comprises tools that go from artificial intelligence (machine learning, deep learning, as well as human cognitive capabilities, exploited through online activities, crowdsourcing, gamification and so on.
Opportunities (and challenges) for MDE
Here is a set of MDSE tasks and tools whose benefits can be especially boosted thanks to cognification.
A modeling bot playing the role of virtual assistant in the modeling tasks
A model inferencer able to deduce a common schema behind a set of unstructured data coming from the software process
A code generator able to learn the style and best practices of a company
A real-time model reviewer able to give continuous quality feedback
A morphing modeling tool, able to adapt its interface at run-time
A semantic reasoning platform able to map modeled concepts to existing ontologies
A data fusion engine that is able to perform semantic integration and impact analysis of design-time models with runtime data
A tool for collaboration between domain experts and modeling designers
A disclaimer
Obviously, we are aware that some research initiatives aiming at cognifying specific tasks in Software Engineering exist (including some activities of ours). But what we claim here is a change in magnitude of their coverage, integration, and impact in the short-term future.
If you want to get a more detailed description, you can go through the detailed post by Jordi Cabot that reports the whole content of the paper.
This is the summary of a joint contribution with Eric Umuhoza to ICEIS 2017 on Model-driven Development of User Interfaces for IoT via Domain-specific Components & Patterns.
Internet of Things technologies and applications are evolving and continuously gaining traction in all fields and environments, including homes, cities, services, industry and commercial enterprises. However, still many problems need to be addressed.
For instance, the IoT vision is mainly focused on the technological and infrastructure aspect, and on the management and analysis of the huge amount of generated data, while so far the development of front-end and user interfaces for IoT has not played a relevant role in research.
On the contrary, we believe that user interfaces in the IoT ecosystem they can play a key role in the acceptance of solutions by final adopters.
In this paper we present a model-driven approach to the design of IoT interfaces, by defining a specific visual design language and design patterns for IoT applications, and we show them at work. The language we propose is defined as an extension of the OMG standard language called IFML.
The slides of this talk are available online on Slideshare as usual:
As model-driven engineering practitioners, we sometimes encounter weird modelling notations for the languages we use… and this is also definitely true for modelling language adopters!
We always end up wondering who could ever think about such or such terrible syntax for a language, also for very well established notations (including, for instance, some pieces of UML or BPMN). I take it for granted this is a common experience (raise your hand if not).
This lead to the idea that also syntax definition should be a more collaborative task. Therefore, we decided to give it a try and test whether crowdsourcing techniques can be used to create and validate language constructs, in particular, its concrete syntax (i.e. notation).
As part of our research work in this area, together with Jordi Cabot’s group, we have setup as an experiment a crowdsourcing campaign using our tool CrowdSearcher.
This boils down to a very simple case: we are asking anyone on the web to look into a very small subset of BPMN, and to participate into 3 simple tasks, including questions for selecting the best notation for some of the BPMN concepts (it won’t take more than 3 minutes).
We asked people to help us responding these 3 quick questions!
Some disclaimers:
1. we don’t care if you are the world’s expert in BPMN or if you never heard about it. We want you!
2. we ask you to register before taking the task (just click on the Register button once you enter the task), simply to make sure we only have one performance per person. All the analysis will run on anonymous data.
3. The results of the survey will be made publicly available in the following months.
To keep updated on my activities you can subscribe to the RSS feed of my blog or follow my twitter account (@MarcoBrambi).
While this is definitely not a statistically significant benchmark, I think it’s a significant insight on the field and on how ourselves (MDE practitioners and researchers) see the field.
Basically, there is absolutely no agreement and common understanding!!
On the question on whether MDE is a sound engineering discipline, one third of responders said yes, one third said no, and one third is not sure. Perfectly even distribution!
In summary, if you don’t count uncertainty, here is what we collected:
I’m happy the panelists raised several issues I pointed out myself in the introduction to the conference: as software modelling scientists, we are facing big challenges nowadays, as the focus of modelling is shifting, due to the fact that now software is more and more pervasive, in fields like IoT, social network and social media, personal and wearable devices, and so on.
Panel included the keynote speakers of the conference: Manfred Broy, Paola Inverardi and Lionel Briand, three well known names in the Software Engineering and Modeling community.
Manfred Broy highlighted:
there is a different between scientific maturity and practical maturity. Sometimes, the latter in companies is far beyond the former.
a truck company in Germany has been practicing modelling for years, and now has this take on the world: whatever is not in the models, doesn’t exist
The current challenges are about how to model cyber-physical systems
The flow of model must be clarified: traceability, refinement, model integration are crucial. You must grant syntactic and semantic coherence
You also need a coherent infrastructure of tools and artefacts, that grants logic integration. You cannot obtain coherence of models without coherence of tools.
You need a lot of automation, otherwise you won’t get practical maturity. This doesn’t mean to have end-to-end, or round-trip complete model transformations, but you need to push automaton as much as possible
Lionel Briand clarified that:
by definition, engineering underpins deep mathematical background as a foundation and implies application of the scientific method to solving problems
maturity can be evaluated in terms of: how much math underpinning is foundational, how many standards and tools exist and are used, whether the scientific approach is used
Tools, methods, engineers, and scale of MDE are increasing (aka. MDE is increasingly more difficult to avoid)
Paola Inverardi recalled a position by Jean Bezivin:
we need to split Domain Engineering (where the problem is) and Support Engineering (where the solution will be)
MDE is the application of modelling principles and tools to any engineering field
So: is actually SOFTWARE the main field of interest of model-driven engineering?
In the modern interpretation of life, covering from smart cities to embedded, wearable, and cyber-physical systems, is the border between the environment and the system still relevant?
In the future we will need to rely less and less on the “creativity” of engineers when building models, and more and more on the scientific/ quantitative/ empirical methods for building models
The debate obviously stirred around this aspects, starting from Bran Selic who asked a very simple question:
Isn’t it the case that the real problem is about the word “modeling”? In any other fields (architecture, mechanics, physics) modelling is implicit and obvious. Why not in our community? At the end, what we want to achieve is to raise abstraction and increase automation, nothing else.
Other issues have been raised too:
why is there so much difference in attitude towards modelling between Europe and US?
what’s the role of notations and standards in the success / failure of MDE?
What’s your take on this issue?
Feel free to share your thoughts here or on Twitter, mentioning me (@MarcoBrambi).
AND:
Respond to my poll on twitter!
To keep updated on my activities you can subscribe to the RSS feed of my blog or follow my twitter account (@MarcoBrambi).
This year I’m involved in the program committee of the Foundations track of ECMFA.
ECMFA 2016 is the 12th European Conference on Modelling Foundations and Applications and is co-located with STAF 2016, on 4-8 July, 2016, in Vienna, Austria. Here are some core excerpts from the call for papers, which could be of interest for software modelling practitioners.
The ECMFA conference series is dedicated to advancing the state of knowledge and fostering the industrial application of Model-Based Engineering (MBE, an approach to the design, analysis and development of software and systems based on high-level models and computer-based automation). Its focus is on engaging the key figures of research and industry in a dialog which will result in stronger and more effective practical application of MBE, hence producing more reliable software based on state-of-the-art research results.
ECMFA 2016 will be co-located with ICMT, TAP, SEFM, ICGT and TTC as part of
the STAF federation of conferences, leading conferences on software
technologies (http://stafconferences.info). The joint organization of
these prominent conferences provides a unique opportunity to gather
practitioners and researchers interested in all aspects of software
technology, and allow them to interact with each other.
ECMFA has two distinct Paper Tracks: one for research papers (Track F)
dealing with the foundations for MBE, and one for industrial/applications
papers (Track A) dealing with the applications of MBE, including experience
reports on MBE tools.
Research Papers (Track F)
In this track, we are soliciting papers presenting original research on all
aspects of MBE. Typical topics of interest include, among others:
Foundations of (Meta)modelling
Domain Specific Modelling Languages and Language Workbenches
Model Reasoning, Testing and Validation
Model Transformation, Code Generation and Reverse Engineering
Model Execution and Simulation
Model Management aspects such as (Co-)Evolution, Consistency, Synchronization
Model-Based Engineering Environments and Tool Chains
Foundations of Requirements Modelling, Architecture Modelling, Platform Modelling
Foundations of Quality Aspects and Modelling non-functional System Properties
Scalability of MBE techniques
Collaborative Modeling
Industrial Papers (Track A)
In this track, we are soliciting papers representing views, innovations and
experiences of industrial players in applying or supporting MBE. In
particular, we are looking for papers that set requirements on the
foundations, methods, and tools for MBE. We are also seeking experience
reports or case studies on the application, successes or current
shortcomings of MBE. Quantitative results reflecting industrial experience
are particularly appreciated. All application areas of MBE are welcomed
including but not limited to any of the following:
MBE for Large and Complex Industrial Systems
MBE for Safety-Critical Systems
MBE for Cyber-Physical Systems
MBE for Software and Business Process Modelling
MBE Applications in Transportation, Health Care, Cloud & Mobile computing, etc. …
Model-Based Integration and Simulation
Model-Based System Analysis
Application of Modeling Standards
Comparative Studies of MBE Methods and Tools
Metrics for MBE Development
MBE Training
Research papers should be up to 16 pages long; Industrial
papers should be 12 pages long (full papers), or 2 pages long (short
papers). Short papers will be given shorter presentation slots.
The authors of selected best papers from the foundations track will be
invited to submit extended version to a special issue of the SoSyM journal
(with another review process).
Important dates for authors:
Abstract submission deadline: February 15, 2016 AoE
Papers submission deadline: March 1, 2016 AoE
Notification to authors: April 7, 2016
Camera ready versions due: April 28, 2016
The complete call for papers is available here in text and here as pdf.
To keep updated on my activities you can subscribe to the RSS feed of my blog or follow my twitter account (@MarcoBrambi).
Marco Brambilla 