WipeOutR: automated redundancy detection for feature models.

Viet-Man Le, Alexander Felfernig, Mathias Uta, Thi Ngoc Trang Tran, Cristian Vidal Silva

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Feature models are used to specify variability and commonality properties of software artifacts. In order to assure high-quality models, different feature model analysis and testing operations can be applied. In this paper, we present two new algorithms that help to make feature model configuration as well as different kinds of analysis operations more efficient. Specifically, we focus on the automated identification of redundancies in feature models and cor-responding test suites. Redundant constraints in feature models can lead to low-performing configuration (solution) search and also to additional efforts in feature model debugging. Redundant feature model test cases can trigger inefficiencies in testing operations. In this paper, we introduce WipeOutR which is an algorithmic approach to support the automated identification of redundancies. This approach has the potential to significantly improve the quality of feature model development and configuration.
Original languageEnglish
Title of host publicationProceedings of the 26th ACM International Systems and Software Product Line Conference
PublisherAssociation of Computing Machinery
Number of pages6
ISBN (Electronic)9781450394437
Publication statusPublished - 12 Sep 2022
Event26th ACM International Systems and Software Product Line Conference: SPLC'22 - Graz, Austria
Duration: 12 Sep 202216 Sep 2022


Conference26th ACM International Systems and Software Product Line Conference
Abbreviated titleSPLC 2022
Internet address


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