AI-guided Model-Driven Embedded Software Engineering

Padma Iyenghar

1,2

, Friedrich Otte

and Elke Pulvermueller

Software Engineering Research Group, University of Osnabrueck, Germany

innotec GmbH, Erlenweg 12, 49324 Melle, Germany

Keywords:

Artiﬁcial Intelligence, Model-Driven Engineering (MDE), Embedded Software Engineering (ESE), Uniﬁed

Modeling Language (UML), Software Development, MDE Tool.

Abstract:

In this paper, an use case of Artiﬁcial Intelligence (AI) empowered Model Driven Engineering (MDE) in the

ﬁeld of Embedded Software Engineering (ESE) is introduced. In this context, we propose to qualify MDE

tools for ESE with an AI assistant or a chatbot. The requirements for the ﬁrst version of such an assistant and

the design concepts involved are discussed. A prototype of such an assistant developed using an open source

conversational AI framework used in tandem with an MDE tool for ESE is presented. Empowering MDE tools

with such AI assistants, would aid novices in MDE or even non-programmer to learn and adopt model-driven

ESE with a not-so-steep learning curve.

1 MOTIVATION

Artiﬁcial Intelligence (AI) is a sub-discipline of com-

puter science which is used to supplement techni-

cal systems with the ability to process tasks indepen-

dently and efﬁciently. With the help of learning algo-

rithms, AI systems can continue learning during on-

going operations, through which the trained models

are optimized and the data- and knowledge-bases ex-

tended. Recent studies on modelling the impact of AI

on the world economy in (Bughin et al., 2018) claim

that AI has large potential to contribute to global eco-

nomic activity. For instance, simulation studies in

(Bughin et al., 2018) show that around 70 percent of

companies may adopt at least one type of AI tech-

nology by 2030 and AI could potentially deliver addi-

tional economic output of around $13 trillion by 2030.

AI is also starting to impact all aspects of the system

and software development lifecycle, from their up-

front speciﬁcation to their design, testing, deployment

and maintenance, with the main goal of helping engi-

neers produce systems and software faster and with

better quality while being able to handle ever more

complex systems. Thus, AI is envisaged to help deal

with the increasing complexity of systems and soft-

ware.

In the direction of the aforesaid context, the Model

Driven Engineering (MDE) paradigm has been intro-

duced in the recent decade with the goal of easing the

developmental complexity of software and systems.

In MDE, models are set in the center of every engi-

neering process. Its target is to guarantee signiﬁcant

rise in productivity, maintenance and interoperability.

It is increasingly used in industry sectors such as Cy-

ber Physical Systems (CPS), automotive and aviation

to name a few. Thus, MDE has been a means to tame,

until now, a part of the aforementioned complexity of

software and systems. However, its adoption by in-

dustry still relies on their capacity to manage the un-

derlying methodological changes, and also the adop-

tion and training with new tools with signiﬁcant cost

and time overhead. In the recently concluded work-

shop on AI and MDE (MDE Intelligence, 2021), it is

identiﬁed that there is a clear need for AI-empowered

MDE, which will push the limits of classic MDE and

provide the right techniques to develop the next gen-

eration of highly complex model-based system.

1.1 Collaboration of AI and MDE

The convergence of two separate ﬁelds in computer

science such as MDE and AI can give rise to collabo-

ration in two main ways such as (a) AI-guided MDE

and (b) MDE for AI. In the following, let us brieﬂy

touch upon the opportunities and challenges derived

by integration of AI and MDE for both (a) and (b).

For (a) AI-guided MDE, MDE can beneﬁt from in-

tegrating AI concepts and ideas to increase its power,

ﬂexibility, user experience and quality. Some oppor-

tunities in this direction can be:

Iyenghar, P., Otte, F. and Pulvermueller, E.

AI-guided Model-Driven Embedded Software Engineering.

DOI: 10.5220/0011006200003119

In Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2022), pages 395-404

ISBN: 978-989-758-550-0; ISSN: 2184-4348

395

• AI planning applied to (meta-) modeling

• Using machine learning of models, meta-models

and model transformation through search-based

approaches

• Self-adapting code generators

• AI-based assistants such as bots or conversational

agents and virtual assistants for MDE tool. AI-

assistant for human-in-the loop modeling, e.g.

dialog-based optimization and support for mod-

eling tasks, answering FAQs and tutorials using

text-to-text, voice-to-text processing, etc.

• Natural language processing applied to modelling

• Semantic reasoning platforms over domain-

speciﬁc models

• AI techniques for data, process and model mining

and categorization

On the other hand, some challenges for (a) could be in

choice, evaluation and adaptation of AI techniques to

MDE, such that they provide a compelling improve-

ment to current system, software modeling and gen-

eration processes. AI-powered MDE should signif-

icantly increase the beneﬁts and reduce the costs of

adopting MDE. Furthermore, this step should also en-

able ease-of-use of MDE tools such that, for instance,

they become analogous to use and popularity of low-

code platforms in the IT sector.

In the case of (b) using MDE for AI, AI can pri-

marily beneﬁt from MDE by integrating concepts and

ideas from MDE such as

• Model-driven processes for AI systems develop-

ment

• Automatic code generation for AI libraries

• Model-based testing of AI artifacts

• Domain-speciﬁc modeling approaches for ma-

chine learning

Rather than signiﬁcant challenges, it is expected that

experts in MDE can make comprehensive inroads in

the AI domain with their rich background and experi-

ence in applying MDE across various sectors.

1.2 MDE in ESE

Software development for embedded systems typi-

cally involves coding in programming language C (or

C++), for a speciﬁc microcontroller. However, in the

recent decade to cope with the growing complexity

of software-intensive embedded systems, MDE has

become an essential part for analysis, design, imple-

mentation and testing of these systems. In the state-

of-the-art, a large variety of software modeling prac-

tices are used in the domain of Embedded Software

Engineering (ESE). A majority of them employ Uni-

ﬁed Modeling Language (UML) (OMG, 2021) as a

ﬁrst choice of a graphical formal modeling language.

A survey in (Akdur et al., 2018), claims that the top

motivations for adopting MDE in ESE (e.g. for CPS

development) are cost savings, achieving shorter de-

velopment time, reusability and quality improvement.

Several state-of-the-practice UML-based MDE

tools are available in the ESE domain. While most

of these tools claim to help build models quickly, edit

programs graphically, generate source code automati-

cally and design systems across platform, they are not

necessarily intuitive to immediately put to use in real-

life projects after installing them (Sundharam et al.,

2021). This makes the learning curve steep and may

introduce high cost and time overhead. A typical use

case of AI-guided MDE can be foreseen for the afore-

said scenario. For instance, to gain higher acceptance

of such MDE tools and bring them a step closer to, for

example, a typical embedded software developer ven-

turing to MDE or even to a non-programmer/beginner

to learn model-driven ESE, AI-based assistants can be

developed and employed together with these tools.

1.3 Novelties

Addressing the aforesaid aspect, we present the fol-

lowing novelties in this short paper:

• Introduce the concept of an AI-assistant or a chat-

bot for an MDE tool in the context of ESE.

• Deﬁne requirements for a ﬁrst version of such a

chatbot and elaborate on the design concepts of

one requirement (step-by-step tutorial).

• Present a prototype of the AI assistant developed

using RASA (Rasa: Open Source Conversational

AI, 2021) for a state-of-the-practice MDE tool in

ESE namely, SiSy (Simple System, 2021).

Following this introduction, related work is presented

in Section 2. The requirements and design concepts

are discussed in section 3. A prototype is presented in

section 4 and section 5 concludes this paper.

2 RELATED WORK

2.1 AI-based Assistants

A chatbot is an AI-based program or an assistant, de-

signed to simulate conversation with human users.

It uses Natural Language Processing (NLP) to com-

municate in human language by text or oral speech

with humans or other chatbots. Chatbots offer users

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396

comfortable and efﬁcient assistance when communi-

cating with them; they provide them with more en-

gaging answers, directly responding to their prob-

lems (Adamopoulou and Moussiades, 2020). A litera-

ture review in (Adamopoulou and Moussiades, 2020)

presents the history, technology and applications of

natural dialog systems or the so-called chatbots.

There are two main approaches in developing a

chatbot, depending on the algorithms and the tech-

niques adopted, namely, pattern matching and ma-

chine learning approaches (Adamopoulou and Mous-

siades, 2020), (Ramesh et al., 2017).

A chatbot can be developed using programming

languages like Java and Python or a chatbot devel-

opment platform that may be commercial or open-

source (Nayyar,D.A., 2019). Open-source platforms

make their code available, and the developer can have

full control of the implementation. Although, com-

mercial platforms do not give full control to develop-

ers, they may still beneﬁt from data for efﬁcient train-

ing of the chatbots.

Open source platforms include Rasa (Rasa: Open

Source Conversational AI, 2021), Botkit (Botkit:

Building blocks for building bots, 2021), Chatterbot

(Chatterbot python library, 2021), Pandorabots (Pan-

dorabots: Chatbots with character, 2021) and Botlyt-

ics (Botlytics:Analytics for your bot, 2021). Com-

mercial platforms include Botsify (Botsify - A Fully

Managed Chatbot Platform To Build AI-Chatbot,

2021), Chatfuel (Chatfuel Chatbot solution, 2021)

and Manychat (Manychat:Chat Marketing Made Easy

with ManyChat, 2021).

Designing highly functional NLUs requires ex-

pert knowledge in machine learning and natural lan-

guage processing. For this reason, several vendors

exist for NLU solutions that make it easier for de-

velopers to create programs with NLU. The currently

most used and evaluated NLUs are (Abdellatif et al.,

2021): Dialogﬂow from Google (Dialogﬂow, 2021),

LUIS from Amazon (LUIS-Language Understanding,

2021), Watson from IBM (Watson Assistant, 2021)

and Rasa which is open source (Rasa: Open Source

Conversational AI, 2021). These do not only con-

sist of NLUs but also of components for building dia-

log managers, which makes them full-ﬂedged chatbot

frameworks. But until now, there are only scientiﬁc

evaluations for the NLU part, because it is of greater

importance and not every application that needs an

NLU also needs a dialog manager.

The results from several evaluation studies show

that the performance of the NLU varies greatly de-

pending on the content domain (Canonico and Russis,

2018), (Angara, 2018), (Shawar and Atwell, 2007).

Therefore, it is important to determine the suitability

of an NLU at the relevant domain. Rasa was specif-

ically evaluated in the context of software engineer-

ing by using technical questions asked on stack over-

ﬂow (Abdellatif et al., 2021). The performance of

the NLUs varied from one aspect to another. There

was no NLU that outperformed the others on every

aspect. In an overall ranking, Watson was placed ﬁrst,

Rasa second, Dialogﬂow third and LUIS fourth. Wat-

son performed best in intent classiﬁcation and entity

extraction, while Rasa performed best in conﬁdence

score. This means that an intent with high conﬁdence

value was more often correct for Rasa than it is with

other NLUs. This makes Rasa very robust for differ-

ent conﬁdence thresholds and allows for effective fall-

back routines. Among the several open source conver-

sational AI framework, we found Rasa to be compre-

hensive and easy-to-use and supported by its elaborate

documentation. Hence, the choice to use Rasa conver-

sational AI framework to build the AI assistant (here-

after referred to as chatbot) for AI-guided MDE in

our work was made. An introduction to Rasa frame-

work is not provided here due to space constraints.

Interested readers are referred to (Rasa: Open Source

Conversational AI, 2021) (Rasa architecture, 2021).

2.2 MDE in ESE: State-of-the-Practice

In the last decade, Model-Driven Architecture (MDA)

introduced by the Object Management Group (OMG)

(OMG, 2021) is considered as the next paradigm shift

in software and systems engineering. Model-driven

approaches aim to shift development focus from pro-

gramming language codes to models expressed in

proper domain-speciﬁc modelling languages. Thus,

models can be understood, automatically manipulated

by automated processes, or transformed into other ar-

tifacts. For instance, in the direction of adoption of

a model-driven approach and the use of simulation-

based techniques, signiﬁcant effort has been spent in

the last decade for easing the development and the

simulation of complex systems using UML/SysML

models in works such as (Bocciarelli et al., 2013),

(Bocciarelli et al., 2019), (Sporer, 2015), (Mhenni

et al., 2018), (Mhenni et al., 2014) and (Andrianari-

son and Piques, 2010) to mention a few. Some of

these works are also joint efforts from industry and

academia.

However, one must admit that the shift from

model-based (models used as mere diagrams) to a

completely model-driven methodology (models used

as central artifacts) in real-life projects in the indus-

try has not yet taken place, especially for ESE do-

main. For example, in a latest survey in (van der

Sanden et al., 2021), a position paper on model-driven

AI-guided Model-Driven Embedded Software Engineering

397

Systems Performance Engineering (SysPE) for Cyber

Physical systems (CPS) is presented. The paper con-

cludes that the state-of-practice is model-based and a

transition to model-driven SysPE is needed to cope

with the ever increasing complexity of today’s CPS.

Some state-of-the-practice UML-based MDE

tools in the embedded software domain include pro-

prietary tools such as Rhapsody Developer (IBM

Software, 2021), Enterprise Architect (Enterprise Ar-

chitect tool, 2021) and SiSy (Simple System, 2021)

and a free UML tool, Visual Paradigm (Visual

Paradigm, 2021). In the non-UML domain, Mat-

lab/Simulink (Mathworks Products, 2021) is among

the most popular MDE tool employed in the embed-

ded software domain. While most of these tools claim

to help build models quickly, edit programs graphi-

cally, generate source code automatically and design

systems across platform, they are not necessarily in-

tuitive to immediately put to use in real-life projects

after installing them.

For the aforementioned scenario, a typical use

case of AI-guided MDE can be foreseen. For in-

stance, to gain higher acceptance of such MDE tools

and bring them a step closer to, for example, a typical

embedded software developer venturing to MDE or

even to a non-programmer/beginner to learn model-

driven ESE, AI-based assistants can be developed and

employed together with these tools.

3 AI ASSISTANT FOR MDE TOOL

As mentioned in section 1.1, in the context of AI

guided MDE, the latter can beneﬁt from integrating

AI concepts to increase its power, ﬂexibility, user ex-

perience and quality. For instance, a starting point can

be development of an AI-assistant such as a chatbot,

which can serve as conversational virtual assistants

for answering FAQs, step-by-step guidance of the tu-

torials available in the MDE tool, modeling tasks and

so on. Such a use case of AI-guided MDE, would help

in enabling ease-of-use of MDE tools (e.g. lessen the

steep learning curve) and perhaps also help to reduce

the costs of adopting MDE.

3.1 Requirements

In the following, requirements for the envisaged AI-

assistant for an MDE tool applied in the context of

ESE are outlined. However, the overall concepts and

ideas discussed here could be applied in the context

of an AI-based assistant for any MDE tool. Please

note that, within the scope of this paper, we make use

of an MDE tool SiSy (Simple System, 2021), aiming

speciﬁcally at MDE for embedded software systems

and envisage the usage of the AI-assistant with this

tool.

3.1.1 R1: Step-by-Step Guided Tutorial

To enable ease-of-use of the MDE tool step-by-step

guidance of tutorials available in the MDE tool can

be offered by the AI-based assistant.

• R1.1-Piece-wise Tutorial: The tutorial should be

provided in a piece-wise manner with step-by step

interactive instructions for the user, based on their

comfort level.

• R1.2-Questions at Any Time: During such

a conversation-based tutorial, the AI-assistant

should be able to answer questions anytime.

• R1.3-Manipulate Tutorial State: The user should

be able to request any tutorial, and should also be

able to stop or restart the running tutorial or switch

to another tutorial. Restarting the tutorial can be

useful if the user accidentally gave false informa-

tion or skipped a tutorial step.

• R1.4-Context Speciﬁc Tutorial: The chatbot

should provide content depending on the context.

3.1.2 R2: Frequently Asked Questions (FAQs)

The chatbot should be able to answer a list of FAQs.

These are typically single-turn interactions, which

means that the user asks a question and the chatbot

can answer in one turn, without additional context in-

formation or asking further questions.

3.1.3 R3: Ease of Use

The chatbot must be easy and intuitive to use. It

must be clear for the user how to request tutorials and

FAQs. This also implies a high robustness for lan-

guage understanding.

3.1.4 R4: Scalability

The chatbot is envisaged to be used in the long run

of the MDE tool. Hence, it is important that it can be

easily adapted, i.e., change and add or remove content

in a simple and fast way.

3.1.5 R5: Integration

The AI Assistant should be easily accessible while us-

ing the MDE tool in question. This can be achieved,

for instance, by integrating it within the MDE tool or

by making it accessible via the web.

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3.1.6 R6: Continuous Improvement

When deployed, the chatbot should continue to col-

lect data and improve its language understanding and

dialog management.

3.2 Design Challenges and Decision

This section discusses the design challenges for re-

quirement 3.1.1 only (due to space constraints), to ar-

rive at a design solution. A chatbot usually comprises

two main components, namely the NLU and dialog

manager. In line with this idea, the proposed design

and architecture of the chatbot introduced in this pa-

per is shown in Figure 1.

Figure 1: Proposed design employing message handling

process of the RASA framework.

The NLU is responsible for understanding the un-

structured user input (text information) and the dialog

manager controls state and ﬂow of the conversation.

As seen in Figure 1, the NLU component takes care of

intent classiﬁcation and entity extraction. The dialog

management component handles conversation track-

ing, ambiguity and error handling and response gener-

ation. The user interface component receives the user

input, and it is communicated to the NLU unit. Based

on the extracted intents and entities, the dialog man-

agement component provides a response to the user

interface component.

3.2.1 Handling Step-by-Step Tutorial

To provide the tutorial step-by-step as discussed in

section 3.1.1, the conversations have to be deﬁned

over multiple turns. In contrast, it would be straight-

forward to provide the conversation in one block,

because this can be a simple question/response pat-

tern. The challenge of providing a step-by-step tuto-

rial is increased by the sub-requirement R.1.2 in sec-

tion 3.1.1, which requires that the questions from the

end-user of the AI assistant should be answered any

time. This prevents a rigid sequential process where

after step one, step two and then step three will fol-

low and so on. This implies a dialog management

(cf. Figure 1), which is designed for multiple-turn di-

alogues, needs to be used. Furthermore, it should be

possible to use slots to save information over multi-

ple turns. Rasa’s dialog management is designed for

multiple-turn dialogues in so far that former intents

can be taken into account when the next action is de-

cided. Furthermore, it is possible to use slots to save

information over multiple turns in Rasa.

With enough training data, the dialog manager

could learn to provide the tutorial steps in the cor-

rect order and ﬂexibly react to other questions at the

same time. But this approach would be very labor-

intensive and there would be no guarantee for success.

Hence, other approaches had to be designed: the ﬁrst

approach was to use forms to manage these multiple-

turn dialogues, the second approach was to use slots

and custom actions. Both these approaches have been

designed and evaluated as part of the concept study

phase and elaborated below.

Option 1: Form Approach. One approach would

be to use forms, since they already are a speciﬁc im-

plementation of multiple turn dialogues. Forms are a

special type of action that allows to deﬁne slots that

need to be ﬁlled. A form can thus be active over

several utterances. As long as there is an empty slot

within the form, the agent will ask a predeﬁned ques-

tion for that slot until it will be ﬁlled and move to the

next slot. If the user gives an utterance that can not be

used to ﬁll the slot, the message will be handled like it

would if there was no active form by the dialog man-

ager. This means that the user can ask the same ques-

tions he or she normally could, as long as the mes-

sage is not confused with valid slot input. Therefore,

requirement R1.2 would be fulﬁlled.

Forms are usually used to collect user information

in a structured fashion. After the bot gives a response,

it will turn back to the form and repeat the question for

the currently empty slot. Figure 2 shows how a form

could be used to implement step-by-step tutorials by

using slots in a slightly different way. The form starts

after the NLU component detects the intent to start

a tutorial. Then, the ﬁrst step of the tutorial will be

provided, and the user will be asked to ﬁll the ﬁrst slot

with something like ”Did you succeed?” or ”Do you

want to continue to the next step?”. Only if the next

AI-guided Model-Driven Embedded Software Engineering

399

Figure 2: Flowchart diagram of the form approach option

to implement step-by-step tutorials.

user input is interpreted as afﬁrmative intent by giving

a message like ”yes” or ”please continue” will the

form continue to the next step. If no afﬁrmative intent

is detected, the message will be handled by the dialog

manager. This is repeated until all tutorial steps have

been provided. If necessary, the ﬁrst slots can be used

to collect context information like controller type to

fulﬁll requirement R1.4.

A prototype evaluation of this approach has shown

that this approach in fact fulﬁlls requirements R1.1

and R1.2 and R3, but also has shown some disadvan-

tages. Some disadvantages are, for each tutorial, a

form has to be created and for each tutorial step, an in-

dividual slot has to be created. Furthermore, for each

form, a FormValidationAction event that includes the

validation logic for each slot, has to be deﬁned. This

makes adding content very time consuming and is in

conﬂict with requirement R4. The effort could prob-

ably be mitigated by a script that adds all these slots

and the validation logic automatically, but this would

have to be updated every time that Rasa changes the

domain syntax or structure or the form structure.

Option 2: Custom Actions Approach. Another

solution could be to use custom actions to provide the

next tutorial step and slots to save the current state of

the tutorial. The tutorial state could be modeled by

the current tutorial name and the current tutorial step.

When a tutorial is requested, a tutorial speciﬁc cus-

tom action will be executed. This will allow handling

tutorial speciﬁc business logic, like different content

depending on the controller type. A specially created

”next” intent will be used to trigger a custom action

”Tutorial dispatcher” that evaluates the current tuto-

rial slot and calls the respective custom action. This

approach is shown in Figure 3.

As in option 1, the process begins after NLU mod-

ule detects the intent to start a tutorial. The custom

action will increment the tutorial step counter slot and

set the slot that deﬁnes the current tutorial. If context

information is needed to provide the tutorial, a form

is run and all the necessary information will be col-

lected. Otherwise, the agent will provide the ﬁrst step

of the tutorial directly. When the user says something

like ”next”, the ”Tutorial Dispatcher” will be called.

The purpose of this action is to request the ”cur-

rent tutorial” slot and dispatch to the tutorial spe-

ciﬁc action. It could also be used to handle tutorial

switches and edge cases. Calling the tutorial speciﬁc

action will increment the tutorial step counter again

and provide the next step. If the user has a question,

the bot will answer it and the slots will remain un-

touched. The tutorial dispatcher decides which cus-

tom action to start, depending on the current tutorial

slot. If a tutorial needs to ask user information, forms

can be used. They can be invoked by the custom ac-

tion before the ﬁrst step, so the collected information

can be used in the subsequent steps.

Decision. As seen above, two approaches are eval-

uated for the requirement R1 in section 3.1.1. Al-

though the form approach utilizes an existing Rasa

tool, it comes with great disadvantages. The process

of adding new tutorials would be very compartmental-

ized, and there is no obvious way to remedy this. The

second approach using custom actions has a similar

problem, because for each tutorial, a custom action

has to be added. But the implementation effort can be

reduced by making use of object inheritance.

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Figure 3: Flowchart diagram of the custom action approach

option to implement step-by-step tutorials.

4 PROTOTYPE

The design alternatives and corresponding design de-

cisions for the requirements mentioned in section

3.1.1-3.1.6 have been implemented in the chatbot pro-

totype. A ﬁrst version of the prototype of the chatbot

can assist the MDE tool (SiSy) user with a set of tu-

torials and answer a set of FAQs, as seen in Figure

Due to space limitations, only the implementation

speciﬁcs of the requirement R1 in section 3.1.1 is pre-

sented in this section.

Figure 4: Welcome screen of the chatbot with a list of its

capabilities.

4.1 Step-by-Step Tutorial

The SiSy tool provides various tutorials to learn

Model-driven implementation of embedded software.

In the ﬁrst version of the prototype, three tutorials

are provided (Figure 4). In this section, the design

and implementation of a message handling mecha-

nism where a multi-step tutorial can be followed, and

the chatbot is able to answer questions at any time is

presented.

Figure 5 shows the design of basic structure of tu-

torial implementation using the LED tutorial exam-

ple (i.e., toggling LEDs in embedded target) example

and described below. Further, the series of steps in

the step-by-step LED tutorial provided piece-wise by

the chatbot for the MDE tool SiSy (as a conversation-

based tutorial) is shown in Figure 6 and Figure 7.

• A Tutorialhhandler class has been implemented

to reduce implementation effort involved in

adding new tutorials (i.e., for scalability, extensi-

bility and continuous improvement). Adding a tu-

torial always requires a new custom action. These

actions are very similar to attributes and functions.

• To add a new tutorial, one has to create a new Sub-

class to the TutorialHandler class and overwrite

the name function and init function. Note that,

the tutorials are split into multiple text messages

and presented to the user based on the user input

during conversation with the chatbot.

• For instance, for the LED Tutorial, a custom ac-

tion handle

led tutorial has been created (see ac-

tions.py in Figure 5). The function of this custom

action is to provide the next step of the LED tuto-

rial. The action gets the current step of the LED

tutorial by querying the tracker for a speciﬁc slot.

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401

Figure 5: Design of basic structure of tutorial handling in

chatbot (here example is LED Tutorial).

If the action was called for the ﬁrst time, it should

ﬁrst start a form.

• The form deﬁnes all information that is needed

by the user in order to provide the correct tutorial

steps. Here the user is asked about the controller

type he has. Based on the user input, (and if the

current step is greater than zero and smaller than

the maximum number of steps), the next tutorial

step will be uttered. The name of the utterance for

each step follow the pattern utter tutorial name

step number.

• Based on the extracted information from user in-

put, the form could be used here to provide ﬁne-

grain instructions tailored to the user information.

If the total number of steps is reached for a tuto-

rial, a congratulation message will be uttered.

• In addition, two rules have been added for the dia-

log manager (see rules.yml in Figure 5). The ﬁrst

rule: there is a ’request led form intent’, the LED

form handler is called. This is how the tutorial

will be started. The second rule: Whenever there

is a ’next’ intent, the ’dispatch tutorials’ action

will be called. This will check which tutorial is

active and dispatch to the correct action.

This architecture described above is ﬂexible since it

allows the user to switch between multiple tutorials.

Whenever the user wants to change the current tu-

torial, the user can type the name of the tutorial he

wishes, and it will start.

5 CONCLUSION

In this paper, an use case of AI-driven MDE is pre-

sented. A proposal to qualify MDE tools for ESE with

an AI assistant or a chatbot is outlined. The require-

ments of a conversational chatbot which uses text (for

input and output) and image (only output) as the con-

versational medium was elaborated. The design al-

ternatives and design choices made to fulﬁl one of

the requirements (step-by-step tutorial) is presented in

this short paper. In the prototype, the design of a ba-

sic structure for message handling mechanisms in the

chatbot is described. It was showcased how a multi-

step tutorial can be followed by the user and how the

chatbot is able to answer questions at any step.

This is only the tip of the iceberg. The bot can

be further improved in umpteen ways. One possible

use case is by making use of training data for the di-

alog management, with the so-called stories. The bot

can help the user at a speciﬁc tutorial step, even if the

user does not ask for speciﬁc information. For ex-

ample an ’unspeciﬁc problem’ intent could be added

with example messages like ’It does not work’, ’noth-

ing works’, ’how does this work?’, ’I have a problem’

and so on. In the next step, the stories could be re-

vised and the responses that the chatbot should give in

the exact situation - depending on the current tutorial,

the current step or even slot values - are added to the

stories. After enough examples, the dialog manager

learns to provide the necessary information even if the

user does not ask for it speciﬁcally-thereby achieving

a true AI guided model-driven development experi-

ence.

MODELSWARD 2022 - 10th International Conference on Model-Driven Engineering and Software Development

402

Figure 6: Step (a)-left and step (b)-right in the step-by-step LED tutorial provided piece-wise by the chatbot for the MDE tool

SiSy.

Figure 7: Step (c)-left and step (d)-right in the step-by-step LED tutorial provided piece-wise by the chatbot for SiSy tool.

AI-guided Model-Driven Embedded Software Engineering

403

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