skip to main content
10.1145/3340495.3342751acmconferencesArticle/Chapter ViewAbstractPublication PagesicseConference Proceedingsconference-collections
research-article

Hospitality of chatbot building platforms

Published:26 August 2019Publication History

ABSTRACT

The temptation to be able to talk to a machine is not new. Recent advancements in the field of Natural Language Understanding has made it possible to build conversational components that can be plugged inside an application, similar to other components. These components, called chatbots, can be created from scratch or with the help of commercially available platforms. These platforms make it easier to build and deploy chatbots, often without writing a single line of code. However, similar to any other software component, chatbots also have quality concerns. Despite significant contributions in the field, an architectural perspective of building chatbots with desired quality requirements is missing in the literature.

In the current work, we highlight the impact of features provided by these platforms (along with their quality) on the application design process and overall quality attributes. We propose a methodological framework to evaluate support provided by a chatbot platform towards achieving quality in the application. The framework, called Hospitality Framework, is based on software architectural body of knowledge, especially architectural tactics. The framework produces a metric, called Hospitality Index, which has utilities for making various design decisions for the overall application. We present the use of our framework on a simple use case to highlight the phases of evaluation. We showcase the process by picking three popular chatbot platforms - Watson Assistant, DialogFlow and Lex, over four quality attributes - Modifiability, Security & Privacy, Interoperability and Reliability. Our results show that different platforms provide different support for these four quality attributes.

References

  1. {n.d.}. Amazon Lex – Build Conversation Bots. https://aws.amazon.com/lex/. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  2. {n.d.}. Blog - Your chatbot and mobile messaging resource - ubisend. https: //blog.ubisend.com/. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  3. {n.d.}. Chatbots.org. https://www.chatbots.org/. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  4. {n.d.}. Dialogflow. https://dialogflow.com/. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  5. {n.d.}. Docs - Dialogflow. https://dialogflow.com/docs. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  6. {n.d.}. IBM Cloud Functions. https://cloud.ibm.com/openwhisk/. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  7. {n.d.}. LUIS (Language Understanding) – Cognitive Services – Microsoft Azure. https://www.luis.ai/home. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  8. {n.d.}. PHP: cGoogle ScholarGoogle Scholar
  9. {n.d.}. RASA. https://rasa.com. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  10. {n.d.}. Stack Overflow - Where Developers Learn, Share, & Build Careers. https: //stackoverflow.com/. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  11. {n.d.}. Watson Assistant - IBM Cloud. https://www.ibm.com/cloud/watsonassistant/. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  12. {n.d.}. Watson Speech to Text. https://www.ibm.com/watson/services/speechto-text/. Accessed: 2019-05-30.Google ScholarGoogle Scholar
  13. Ashish Agrawal and TV Prabhakar. 2013. Hospitality of cloud platforms. In Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM, 389–396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Felix Bachmann, Len Bass, and Robert Nord. 2007. Modifiability tactics. Technical Report. CARNEGIE-MELLON UNIV PITTSBURGH PA SOFTWARE ENGINEERING INST.Google ScholarGoogle Scholar
  15. Len Bass, Paul Clements, and Rick Kazman. 2013. Software Architecture in Practice.Google ScholarGoogle Scholar
  16. Valerie Belton and Theodor Stewart. 2002. Multiple criteria decision analysis: an integrated approach. Springer Science & Business Media.Google ScholarGoogle Scholar
  17. Rene Berger, Markus Ebner, and Martin Ebner. 2019. Conception of a Conversational Interface to Provide a Guided Search of Study Related Data. International Journal of Emerging Technologies in Learning 14, 7 (2019).Google ScholarGoogle ScholarCross RefCross Ref
  18. Pierre Bourque, Richard E Fairley, et al. 2014. Guide to the software engineering body of knowledge (SWEBOK (R)): Version 3.0. IEEE Computer Society Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Daniel Braun, Adrian Hernandez-Mendez, Florian Matthes, and Manfred Langen. 2017. Evaluating Natural Language Understanding Services for Conversational Question Answering Systems. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue. Association for Computational Linguistics, Saarbrücken, Germany, 174–185.Google ScholarGoogle ScholarCross RefCross Ref
  20. D. Braun and F. Matthes. 2019. Towards a Framework for Classifying Chatbots. In Proceedings of the 21th International Conference on Enterprise Information Systems - Volume 1: ICEIS.Google ScholarGoogle Scholar
  21. Frank Buschmann, Regine Meunier, Hans Rohnert, Peter Sommerlad, and Michael Stal. 1996. Pattern oriented software architecture: A system of patterns. Hoboken. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Massimo Canonico and Luigi De Russis. 2018. A comparison and critique of natural language understanding tools. In Cloud Computing 2018. 110–115.Google ScholarGoogle Scholar
  23. Robert Dale. 2016. The return of the chatbots. Natural Language Engineering 22, 5 (2016), 811–817.Google ScholarGoogle ScholarCross RefCross Ref
  24. Laurent Deruelle, Mustapha Derras, Jordi Cabot, and Gwendal Daniel. 2019. Multi-Platform Chatbot Modeling and Deployment with the Jarvis Framework.Google ScholarGoogle Scholar
  25. Ching-Lai Hwang and Kwangsun Yoon. 2012. Multiple attribute decision making: methods and applications a state-of-the-art survey. Vol. 186. Springer Science & Business Media.Google ScholarGoogle Scholar
  26. ISO/IEC. 2010. ISO/IEC 25010 System and software quality models. Technical Report.Google ScholarGoogle Scholar
  27. Rick Kazman, Len Bass, Gregory Abowd, and Mike Webb. 1994. SAAM: A method for analyzing the properties of software architectures. In Proceedings of 16th International Conference on Software Engineering. IEEE, 81–90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Rick Kazman, Mark Klein, Mario Barbacci, Tom Longstaff, Howard Lipson, and Jeromy Carriere. 1998. The architecture tradeoff analysis method. In Proceedings. Fourth IEEE International Conference on Engineering of Complex Computer Systems (Cat. No. 98EX193). IEEE, 68–78.Google ScholarGoogle ScholarCross RefCross Ref
  29. Xingkun Liu, Arash Eshghi, Pawel Swietojanski, and Verena Rieser. 2019. Benchmarking Natural Language Understanding Services for building Conversational Agents. arXiv e-prints, Article arXiv:1903.05566 (Mar 2019), arXiv:1903.05566 pages. arXiv: cs.CL/1903.05566Google ScholarGoogle Scholar
  30. Dijana Peras. {n.d.}. Chatbot evaluation metrics. In Economic and Social Development: Book of Proceedings, 89-97.Google ScholarGoogle Scholar
  31. Philip Resnik and Jimmy Lin. 2010. Evaluation of NLP Systems. John Wiley & Sons, Ltd, Chapter 11, 271–295.Google ScholarGoogle Scholar

Index Terms

  1. Hospitality of chatbot building platforms

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SQUADE 2019: Proceedings of the 2nd ACM SIGSOFT International Workshop on Software Qualities and Their Dependencies
        August 2019
        38 pages
        ISBN:9781450368575
        DOI:10.1145/3340495

        Copyright © 2019 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 26 August 2019

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Upcoming Conference

        ICSE 2025

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader