Generating Feature Models from Requirements: Structural vs. Functional Perspectives

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


Adoption of SPLE techniques is challenging and expensive. Hence, automation in the adoption process is desirable, especially with respect to variability management. Different methods have been suggested for (semi-)automatically generating feature models from requirements or textual descriptions of products. However, while there are different ways to represent the same SPL in feature models, addressing different stakeholders' needs and preferences, existing methods usually follow fixed, predefined ways to generate feature models. As a result, the generated feature models may represent perspectives less relevant to the given tasks. In this paper we suggest an ontological approach that measures the semantic similarity, extracts variability, and automatically generates feature models that represent structural (objects-related) or functional (actions-related) perspectives. The stakeholders are able to control the perspective of the generated feature models, considering their needs and preferences for given tasks.

Original languageEnglish
Title of host publicationProceedings of the 18th International Software Product Line Conference
Subtitle of host publicationCompanion Volume for Workshops, Demonstrations and Tools
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450327398
StatePublished - 15 Sep 2014
Event18th International Software Product Line Conference, SPLC 2014 - Florence, Italy
Duration: 15 Sep 201419 Sep 2014


Conference18th International Software Product Line Conference, SPLC 2014


  • Feature models
  • Mining
  • Ontology
  • Reverse engineering
  • Semantic similarity

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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