The document discusses the development of Xatkit, a low-code chatbot platform created to simplify the complexity of chatbot design and implementation. It highlights the challenges faced during its creation, including software integration, maintenance, and attracting contributors while emphasizing the need for model-driven solutions in chatbot technology. Xatkit aims to serve as both a teaching and research tool for chatbot development and software engineering.

1. Lessons learned from building a
commercial bot development
platform
Jordi Cabot & Gwendal Daniel
@softmodeling @xatkit – xatkit.com

2. Xatkit is a spin-off of the SOM team

3. 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
Create Better Software Faster

4. Better
Software
Faster
Low-code
development
Artificial
Intelligence
Open Source
+ Open Data

5. Bots vs chatbots vs voicebots

6. What is exactly a Chatbot?
• Chatbot = Bot + Chat interface
• Voicebot = Bot + Voice Interface
• At the core, a NLU/NLP engine with two key missions
– Classify the user utterance (i.e. input text) in one of the intents
(set of possible questions/requests) defined in the bot
– Identify the parameters (called « entities ») from the utterance
Voicebot
Chatbot
Bot

7. Chatbots combine
all the complexity
of bots with the
complexity of NL
(typos, trolls, sentiment
analysis…)

8. Think twice before building a chatbot

9. Can you help us create a chatbot to help
newcomers to write good bug reports on GitHub?
Sure! It’s just a set of questions &
answers, this will be pretty simple!
Narrator It wasn’t.
Once upon a time (we were young and naïve)

10. What went wrong? – Many pieces to assemble
Input/Output
Messaging channels
External
Platforms
NLU Engine

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

12. You’d like to focus on writing bot rules
User Intent
Action Parameters
If the User Wants To Open Issue
Reply « In which repository? » on Slack
Platform

13. Why Xatkit?

14. The AI
ecosystem
is huge

15. >100 chatbot platforms
• Rasa, BotPress, Chatfuel, Inbenta, Botsify,
Flow XO,…
• And quite a few NLU providers / engines:
DialogFlow, Lex, LUIS, Watson, NLP.js,
Stanford…

16. … but
• Only around 15% are open source
• Not flexible (i.e. difficult to integrate other platforms)
• NLU Lock-in
• Low-level (beyond simple Q&A bots, you quickly end
up programming in JS)

17. • 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/chatbots ->
Chatbot Modeling to the rescue!

18. We were asked to build a bot. We ended up
building a bot platform

19. The Xatkit solution

20. Key Concepts

21. Model-driven / Low-code solution
• Raising the level of abstraction at what chatbots are defined
• DSLs to focus on the core logic of the chatbot
• Conversation and user interactions
• Action computations
• Independent from specific implementation technologies
• Automating the deployment and execution of the modeled chatbot
• Deploy over multiple platforms
• Runtime to execute the chatbot logic

22. Platforms in Xatkit
• An abstraction of any input/output messaging tool and any external
service. Platform interfaces are also defined with a DSL
• Platforms offer actions (that the bot can execute) and events (whom
the bot can subscribe to)
• PIM/PSM : Bots can be defined using generic platforms that are then
concretely instantiated during the deployment. No need to change
the bot

23. Xatkit Framework
Chatbot
Designer
Intent Recognition NLU Providers
(platform-specific)
Platform 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
Daniel, Cabot, Deruelle, Derras:
Xatkit: A Multimodal Low-Code
Chatbot Development Framework.
IEEE Access 8: 15332-15346 (2020)

24. Xatkit By Example

25. Example platforms
Abstract platform Chat
actions {
PostMessage(message)
Reply(message)
}
}
Platform Slack extends Chat
actions {
PostMessage(message, channel)
Reply(message)
}
}

26. Ex1:
Stargazer

28. Ex2:
Talk with
your repos
from Slack

30. Xatkit Tool

31. Tool Support
• Available on GitHub as an independent organization:
https://github.com/xatkit-bot-platform/
• >40 repositories, thousands of commits,…
• Open source (EPL v2)
• With tutorials and examples to help you get started building bots!

32. Reflections / Lessons Learned
(May 2018 - )

33. From a proof-of-concept to a real tool

34. Organizational / Development

35. You need to be a Polymath to create a bot platform
• Plenty of core bot tech: NLU, Sentiment Analysis, Xtext, Slack/GH/… APIs,….
• But even more non-bot tech: InfluxDB, Nginx, eCharts, React, web sockets, Docker…
• Commercial-grade quality involves spending more and more time on “boring” stuff
Bruneliere, Hugo, Jordi Cabot, Frédéric Jouault, Massimo Tisi,
and Jean Bézivin. "Industrialization of research tools: The atl
case." 2010.

36. Common challenges to any OSS project
• Attracting contributors
• Giving support to users that don’t even say thank you
• Cost of maintaining external contributions (e.g. connectors) especially
when the core is still evolving
Being pragmatic (ROI)
• We went from having official releases, to core releases to no releases
(compile from sources). Moving to Docker images (good compromise)
• Also, no support for older versions.
• No backward compatibility.

37. Technical

38. Eat your own chatbot food for a better language
• Evolving your DSLs is expensive, try to design a variety of bots to make
sure your language is expressive enough for your needs
• We had to move from a partial order language to a state machine one
(and change the examples, the docs, the editor,…) with it
• Set on a language granularity (e.g. live chat primitive)
Combine textual and graphical notations
• Textual for writing + Graphical for Reading
• Do not reinvent the wheel -> We came close to reinvent Java (and moved
to xbase)

39. Choose your stack carefully
• We chose Java, Eclipse/EMF and Xtext as this was our “natural” option
• Changing now would imply almost starting from scratch
Think long-term
• Research prototypes are pure technical debt
• MapDB -> InfluxDB
• Scalability: multi-user/multi-session bots

40. DDoS attacks via a bot
• Easy to attack your bot simulating a massive number of utterances
• Could also be expensive (e.g. DialogFlow API)
Other security concerns
• Must provide support for offline chatbots
• Privacy of the monitoring data, especially if linked to specific users

41. NLP

42. NLP engines don’t speak Catalan
• You may need to combine more than 1 NLP provider to support less
mainstream languages
NLP engines are not bilingual
• Bots should be able to combine different languages in the same
conversation

43. Everybody seems interested in having sex with a bot:
• Trolls are a huge problem with bots
• As a Brand you don’t want to risk going viral because your bot gave the
wrong response to a Troll question
Importance of intent libraries
• To deal with troll questions but also for greetings, thank you, goodbye,…
and other common intents you don’t want to define again and again

44. Testing chatbots is a largely unexplored area
• We use Botium to record conversations to be used as test cases
• But we need much more (e.g. automatic generation of test cases)
Quality of the intent matching process
• It can decrease just because your NLP provider releases a new version
• More is not always better (e.g. adding more training sentences may end
up conflicting with another intent)
• Enumerations (“entities”, e.g. list of cities) make a huge difference

45. Business

46. End-users do not want to define chatbots:
• Doesn’t matter how nice is your inteface
• Doesn’t matter how high-level is your language
• Independent consultants DO want that (who are you selling to?)
End-users just want to have fun give you their data
• And hope you’ll transform those excels, FAQs, guides… into a bot
• Selling a service vs selling a tool

47. Find your niche
• You cannot compete with Microsoft or DialogFlow. Nor with Zapier
From an Open Core model to a Chatbot as a Service
• We didn’t feel there was a market for selling add-ons
• Moving to tailor made bots thanks to Xatkit flexibility + ChaaS for
premade ones (e.g. Xatkit for WordPress plugin)
• DISCLAIMER: We’re still looking for the good niche + business model
combination

48. Understand what bot owners are looking for
• E.g. making sure the color of the Xatkit widget matches the look&feel of
the website is more important than any item in your roadmap
Help them understand what their visitors want to use the bot for
• Importance of the bot monitoring (wrong training sentences, unexpected
questions,…)
• (Supervised) bot learning as automatic bot learning is risky

49. Legal

50. You sure you want to create a spin-off?
• 1 year of paperwork
• University takes a cut of the company and limits your dedication
• IP is problematic (also among team members)
• It may limit your publication strategy
• (Your mileage may vary)
Look for win-win situations
• Even if the spin-off fails everybody should get something out of it
(publications, better CV,…)

51. Where do we go from here

52. Xatkit as a bot teaching platform
• Do you teach any course involving bots, chatbots or NLP? Or DSLs /
software modeling ?
• And are looking for a use-case or a ready-to-use platform?
• We’ll be happy to help
• Materials
• Assistance
• (Remote) seminar

53. Xatkit as a research platform
• Researching on the impact of (chat)bots in Software Development?
Combining a number of different platforms?
• Do not reinvent the wheel, adopt Xatkit as base platform
• Benefit from all Xatkit features and connectors to have powerful bots with
less effort
• Spend more time on the core research aspects

54. Xatkit as a research platform
• I think we all agree that there is plenty of research to be done on
• Software engineering for bots
• Bots for software engineering
• Examples:
• Testing for bots
• Automatic generation of bots from data source (e.g. open data)
• Bots to promote diversity and inclusion (or to detect online harassment)
• …

55. Let’s work
together
jordi.cabot@
icrea.cat
@softmodeling
/ @xatkit
xatkit.com