1.
Modeling and AI: Friends or foes
Jordi Cabot
@softmodeling – jordicabot.com June 2019

2.
About

3.
SOM Research Lab
Software runs the world. Models run the software

4.
Our mission
We are interested in the
broad area of systems and
software engineering,
especially promoting the
rigorous use of models and
engineering principles
while keeping an eye on the
most unpredictable element
in any project: the people.
Flickr/clement127

5.
The AI subteam

6.
Preliminaries

7.
Model-Driven Engineering

8.
https://mdse-book.com/

9.
To model, or not to model, this is the WRONG
question
- Shakespeare

10.
Artificial Intelligence

11.
Cognification:
The application of knowledge
to boost the performance
and impact of a process

12.
Cognification
Artificial
Intelligence
Machine
Learning
Deep Learning

13.
© Prof. Dr. Andreas Vogelsang – TU Berlin

14.
Why should you care?

15.
Kai-Fu Lee

16.
AI
Coder

17.
Modeler

18.
How can we be friends:
(1) MDE for AI

19.
• Grady Booch – history of softwre engineering
The entire history of software engineering is that of
the rise in levels of abstraction
- Grady Booch
Also true for AI -> MDE is the
best tool for this evolution

20.
AI is software, too! (and rather immature)

21.
AI
landscape
Could
B from
DSLs
PIM/PSM
M2M
M2T
…

22.
Modeling ML pipelines (data
preparation is more challenging
than the learning itself)

23.
As expected, all players are moving in
this direction
• Key to get more users that will then become
users of their underlying infrastructure
Azure ML Studio
Orange
RapidMiner
IBM SPSS Modeler
Nvidia digits
Knime

24.
Model-Driven Bot Development:
Xatkit
https://github.com/xatkit-bot-platform
Gwendal Daniel, Jordi Cabot, Laurent Deruelle, Mustapha Derras:
Multi-platform Chatbot Modeling and Deployment with the Jarvis
Framework. CAiSE 2019: 177-193

25.
Let’s create a chatbot to help
newcomers to write issues on Github!
Alright! It’s just a set of questions &
answers, this will be pretty simple!
Narrator It wasn’t.
Once upon a time

26.
What went wrong?
if(‘’ I have an issue’’) {
return ‘’In which
repository?’’
}

27.
Chatbots are complex systems
Conversation
Logic
Text Processing
External Services
Messaging
Platforms
Deployment
Evolution
Maintenance
Tests

28.
AI Chatbot
landscape

29.
MDD of (chat)bots with Xatkit
• Raise the level of abstraction at what chatbots are
defined
– Focus on the core aspects of the chatbot
• Conversation and user interactions
• Action computations
– Independent from specific implementation technologies
• Automate the deployment and execution of the
modeled chatbot over multiple platforms

30.
Chatbot dev – Example: Bots for OSS
Slack bot to help users open a well-described new issue on a GitHub
project
If the User Wants To Open Issue
Reply « In which repository? » on Slack
User Intent
Action Parameters Platform
Conversation
step

31.
Intent language
Library example
intent OpenNewIssue {
inputs {
‘’I want to create an issue’’
‘’Open an issue’’
}
}
intent SpecifyRepository {
inputs {
‘’In repository MyRepo’’
}
outContext Repository {
name <- ‘’MyRepo’’ (@any)
}
}

32.
Execution Language
Import library Example
Import platform Slack
Import platform Github
Listen to Slack
on intent OpenNewIssue do
Slack.reply(‘’Sure, I’ll help you to write
your issue! Which repository is for?’’)
on intent SpecifyRepository do
Slack.reply(‘Great, I’ll open an issue
related to repository {$Repository.name}.’’)
[… a few intents later with all the info …]
Github.openIssue({$Repository.name},
{$Issue.title}, {$Issue.content})

33.
Platform Language
Abstract platform Chat
actions {
PostMessage(message, channel)
Reply(message)
}
}
Platform Slack extends Chat Platform Github
action {
openIssue(repository, title, content)
}
}

34.
Xatkit Framework
Chatbot
Designer
Intent Recognition Providers
(platform-specific)
Platorm Package
Intent Package
Xatkit Modeling
Language Chatbot
User
Instant
Messaging
Platforms
Xatkit Runtime
Execution Package
uses
uses
Platform-independent
chatbot definition
External
Services
Deployment
Configuration
Platform
Designer

35.
How can we be friends:
(2) AI for MDE

36.
AI to simplify development

37.
Smart Autocomplete

38.
Smart Autocomplete
https://ai.facebook.com/blog/aroma-ml-for-
code-recommendation/

39.
Also in MDE land....
Jordi Cabot, Robert Clarisó, Marco Brambilla, Sébastien Gérard:
Cognifying Model-Driven Software Engineering. STAF Workshops 2017: 154-160

40.
Mendix Assist

41.
OutSystems.ai

42.
Many other useful applications to explore
Modeling bot as virtual assistant (kind of an “expert system”)
Model inferencer to discover schema of unstructured data
A code generator that mimicks a company programming style
A real-time model reviewer ( “pair-modeling” )
A morphing modeling tool that adapts to the user profile
(expertise, typical usage)

43.
AI to skip modeling:
An ML-based approach to learn
model transformations
Loli Burgueño, Jordi Cabot and Sebastien Gerard. An LSTM-Based Neural
Network Architecture for Model Transformations

44.
Model transformations chain
Original model
… CODE
Software code1 refinementst n refinementth
Model-to-model
Transformation
Model-to-text
Transformation
CODE
CODE
• Requires learning a new language (the MT Language)
• Time consuming
• Error prone

45.
Let’s try to learn the MTs automatically
Input
Output
Training Transforming
ML Input OutputML
Machine Learning
Artificial Neural Networks
Deep Networks
Recurrent networks
LSTM
BPMN
Petri nets
BPMN Petri Net

46.
MTs ≈ sequence-to-sequence arch
• Combine two LSTM for better results
• Avoids fixed size input and output constraints

47.
Tree-to-Tree Encoder-decoder arquitecture
InputTree
EmbeddingLayer
Encoder
LSTM network
OutputTree
ExtractionLayer
Decoder
LSTM network
AttentionLayer
InputModel
OutputModel

48.
• Pre- and post-processing required to…
• represent models as trees
• reduce the size of the training dataset by using a canonical form
• rename variables to avoid the “dictionary problem”
InputModel
(preprocessed)
InputTree
EmbeddingLayer
Encoder
LSTM
network
OutputTree
ExtractionLayer
OutputModel
(non-postprocessed)
Decoder
LSTM
network
AttentionLayer
InputModel
OutputModel
Preprocessing
Postprocessing
Complete picture ML is
NOT a
fast
learner!

49.
AI for model management:
Model similarity
with Graph Kernels
Robert Clarisó, Jordi Cabot:
Applying graph kernels to model-driven engineering
problems. MASES@ASE 2018: 1-5

50.
Model diversity for better testing

51.
If I ask a
solver for
25
solutions, I
only get
three
REALLY
different
ones!

52.
Graph-based ML for more diverse results

53.
Feature-based methods
• Graphs summarized as feature vectors
• The choice of the features is key
• E.g. of features: number of vertices, edges,
avg/min/max degree…
Easy to use BUT loss of precision

54.
Kernel-based methods
• Goal
– Polynomial times
– Capture relevant topological information
• How
– Compare graph structures: path, subtrees, cycles
– Different Kernel methods. E.g. random walks
Best Kernel depends on the problem!

55.
Let’s
revisit this
problem
Kernels to
cluster the
solutions and
choose
representative
examples

56.
The leader of a dept works
in a different dept (error?)
Might be missed by
feature vector encoding

57.
(MDE <-> AI)
meta

58.
A model-
based bot
used to
build
models

59.
Also for
BPMN
models
Joint work with Josep Carmona’s team
(go to see From Process models to chatbots on Wed)

60.
Smart
modeling
tools to
generate
smart apps

61.
Open Challenges

62.
All this is very
fancy and
fashionable
BUT

63.
Where is my data?
Every few years, a new initiative to collect
models in a repo pops up but in the end…
Data augmentation techniques? (mutation, GANs,…)

64.
• Size of the training dataset
• Diversity in the training set
• Computational limitations of ANNs
– i.e., mathematical operations
• Generalization problem
– predicting output solutions for input models very different from
the training distribution it has learnt from
Limits of Neural Networks

65.
Models
Community

66.
Modeling needs to
adapt
- AI is here to stay
- Let’s see it as an opportunity
- To showcase the benefits of
modeling
- To improve the modeling
experience and broaden its user
base

67.
Models are not
enough
We need (smart)
processing
capabilities to get the
most out of themhttps://modelia.eu/

68.
Summary

69.
Exciting times to be a modeler!!

70.
Let’s work
together
jordi.cabot@
icrea.cat
@softmodeling
modeling-
languages.com

71.
Back-up slides

72.
Model representation
MODEL
ASSOCOBJ
c
Class
ATTS
isAbstract name
false family
OBJ
a
Attribute
ATTS
multivalued name
false surname
OBJ
d
t
Datatype
ATTS
name
String
at
t
c a
ASSOC
typ
e
a d
t

73.
Preliminary results
• Correctness
– Measured through the accuracy and validation
loss

74.
Preliminary results
• Performance
1. How long does it
take for the training
phase to complete?

75.
Preliminary results
• Performance
1. How long does it
take for the training
phase to complete?
2. How long it takes to transform an
input model when the network is
trained?