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Interactive feature modeling with background knowledge for validation and configuration

Published: 12 September 2022 Publication History

Abstract

Feature modeling enables a straightforward representation of a product's features, components, and the relations between them. In this way, feature models serve as an excellent approach to diagrammatically model a product design for manufacturing purposes. However, the actual usage of such a feature model to generate suitable designs in the context of real-life industry applications is often limited, as crucial background knowledge cannot be expressed. Moreover, even though expert validation of a feature model is an important aspect of its creation, current tooling often falls short on this aspect. Indeed, although state-of-the-art tools are capable of generating possible configurations, this is not sufficient to completely validate complex applications: instead, we should enable the expert to interactively explore the problem domain. In this paper, we present our feature modeling tool, called FM-IDP, which aims to overcome both of these shortcomings. In FM-IDP, background knowledge can be expressed in FO(·), a rich extension of classical first-order logic. Using an off-the-shelf logical reasoning engine and an integrated interactive configuration interface, modelers can interact with the feature model and its background knowledge to explore the problem space on-the-fly. We motivate our approach using an industrial use case focused on real-life component design.

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Cited By

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  • (2024)Semantic configuration model with natural transformationsCognitive Systems Research10.1016/j.cogsys.2023.10118583:COnline publication date: 4-Mar-2024
  • (2023)Facilitating Investment Strategy Negotiations Through Logic2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10372044(103-108)Online publication date: 5-Dec-2023

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Published In

cover image ACM Conferences
SPLC '22: Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume B
September 2022
246 pages
ISBN:9781450392068
DOI:10.1145/3503229
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 the author(s) 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].

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Publication History

Published: 12 September 2022

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Author Tags

  1. IDP
  2. background knowledge
  3. feature modeling
  4. interactive configuration

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SPLC '22 Paper Acceptance Rate 14 of 41 submissions, 34%;
Overall Acceptance Rate 167 of 463 submissions, 36%

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Cited By

View all
  • (2024)Semantic configuration model with natural transformationsCognitive Systems Research10.1016/j.cogsys.2023.10118583:COnline publication date: 4-Mar-2024
  • (2023)Facilitating Investment Strategy Negotiations Through Logic2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10372044(103-108)Online publication date: 5-Dec-2023

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