---
_id: '29000'
abstract:
- lang: eng
  text: "This thesis aims to provide a bidirectional chatbot solution for the requirement
    engineering process. The Sonderforschungsbereich (SFB) 901 intends to provide
    the composition of software service On-the-Fly (OTF). The sub-project (B1) of
    the SFB 901 project deals with the parameters of service configuration. OTF Computing
    aims to eradicate the dependency on the requirement engineers for the software
    development process. However, there is no existing bidirectional chatbot solution
    that analyses user software requirements and provides viable suggestions to the
    user regarding their service. Previously, CORDULA chatbot was developed to analyze
    the software requirements but cannot keep the conversation’s context. The Rasa
    framework is integrated with the knowledge base to solve the issue, the knowledge
    base provides domain-specific knowledge to the chatbot. The software description
    is passed through the natural language understanding process to give consciousness
    to the chatbot. This process involves various machine learning models, including
    app family classification, to correctly identify the domain for user OTF service.
    The statistical models like naïve Bayes, kNN and SVM are compared with transformer
    models for this classification task. Furthermore, the entities (functional requirements)
    are also separated from the user description.\r\nThe chatbot provides the suggestion
    of requirements from the preliminary service template with the support of the
    knowledge base. Furthermore, the generated response is compared with the state-of-the-art
    DialoGPT transformer model and ChatterBot conversational library. These models
    are trained over the software development related conversational dataset. All
    the responses are ranked using the DialoRPT model, and the BLEU score to evaluates
    the models’ responses. Moreover, the chatbot mod- els are tested with human participants,
    they used and scored the chatbot responses based on effectiveness, efficiency
    and satisfaction. The overall response accuracy is also measured by averaging
    the user approval over the generated responses."
author:
- first_name: Mobeen
  full_name: Ahmed, Mobeen
  last_name: Ahmed
citation:
  ama: Ahmed M. <i>Knowledge Base Enhanced &#38; User-Centric Dialogue Design for
    OTF Computing</i>.; 2022.
  apa: Ahmed, M. (2022). <i>Knowledge Base Enhanced &#38; User-centric Dialogue Design
    for OTF Computing</i>.
  bibtex: '@book{Ahmed_2022, title={Knowledge Base Enhanced &#38; User-centric Dialogue
    Design for OTF Computing}, author={Ahmed, Mobeen}, year={2022} }'
  chicago: Ahmed, Mobeen. <i>Knowledge Base Enhanced &#38; User-Centric Dialogue Design
    for OTF Computing</i>, 2022.
  ieee: M. Ahmed, <i>Knowledge Base Enhanced &#38; User-centric Dialogue Design for
    OTF Computing</i>. 2022.
  mla: Ahmed, Mobeen. <i>Knowledge Base Enhanced &#38; User-Centric Dialogue Design
    for OTF Computing</i>. 2022.
  short: M. Ahmed, Knowledge Base Enhanced &#38; User-Centric Dialogue Design for
    OTF Computing, 2022.
date_created: 2021-12-16T15:13:07Z
date_updated: 2023-05-02T13:25:45Z
ddc:
- '004'
department:
- _id: '600'
file:
- access_level: closed
  content_type: application/pdf
  creator: jkers
  date_created: 2023-05-02T13:25:27Z
  date_updated: 2023-05-02T13:25:27Z
  file_id: '44325'
  file_name: Thesis-Report-MOBEEN-AHMED-6856465-Knowledge_Base_Enhanced___User_centric_Dialogue_Design_for_OTFComputing.pdf
  file_size: 3092211
  relation: main_file
  success: 1
file_date_updated: 2023-05-02T13:25:27Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '1'
  name: SFB 901
- _id: '3'
  name: SFB 901 - Project Area B
- _id: '9'
  name: SFB 901 - Subproject B1
publication_status: published
status: public
supervisor:
- first_name: Joschka
  full_name: Kersting, Joschka
  id: '58701'
  last_name: Kersting
title: Knowledge Base Enhanced & User-centric Dialogue Design for OTF Computing
type: mastersthesis
user_id: '58701'
year: '2022'
...