Learning Contextual-Variability Models

Paul Temple, Mathieu Acher, Jean-Marc Jézéquel, Olivier Barais

Research output: Contribution to journalArticlepeer-review


Modeling how contextual factors relate to a software system's configuration space is usually a manual, error-prone task that depends highly on expert knowledge. Machine-learning techniques can automatically predict the acceptable software configurations for a given context. Such an approach executes and observes a sample of software configurations within a sample of contexts. It then learns what factors of each context will likely discard or activate some of the software's features. This lets developers and product managers automatically extract the rules that specialize highly configurable systems for specific contexts.

Original languageEnglish
Article number8106868
Pages (from-to)64-70
Number of pages7
JournalIEEE Software
Issue number6
Publication statusPublished - 13 Nov 2017


  • contextual variability
  • contextual-variability modeling
  • machine learning
  • software development
  • software engineering


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