STARS: software technology for adaptable and reusable systems

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Dynamic Software Product Lines (DSPLs) engineering implements self-adaptive systems by dynamically binding or unbinding features at runtime according to a feature model. However, these features may interact in unexpected and undesired ways leading to critical consequences for the DSPL. Moreover, (re)configurations may negatively affect the runtime system's architectural qualities, manifesting architectural bad smells. These issues are challenging to detect due to the combinatorial explosion of the number of interactions amongst features. As some of them may appear at runtime, we need a runtime approach to their analysis and mitigation. This thesis introduces the Behavioral Map (BM) formalism that captures information from different sources (feature model, code) to automatically detect these issues. We provide behavioral map inference algorithms. Using the Smart Home Environment (SHE) as a case study, we describe how a BM is helpful to identify critical feature interactions and architectural smells. Our preliminary results already show promising progress for both feature interactions and architectural bad smells identification at runtime.
Original languageEnglish
Title of host publicationSPLC '21
Subtitle of host publicationProceedings of the 25th ACM International Systems and Software Product Line Conference - Volume B
EditorsMohammed Mousavi, Pierre-Yves Schobbens
Place of PublicationLeicester
PublisherACM Press
Number of pages5
ISBN (Print)978-1-4503-8470-4
Publication statusPublished - 6 Sep 2021


  • Software Product Line Engineering
  • Dynamic Software Product Lines Engineering
  • Self-adapting system
  • Software architecture
  • MAPE-K loop
  • Software testing


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