Automated diagnosis of feature model configurations

  • Authors:
  • J. White;D. Benavides;D. C. Schmidt;P. Trinidad;B. Dougherty;A. Ruiz-Cortes

  • Affiliations:
  • Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA;Department of Computer Languages and Systems University of Seville, Avda. de la Reina Mercedes, s/n B-41012 Seville, Spain;Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37204, USA;Department of Computer Languages and Systems University of Seville, Avda. de la Reina Mercedes, s/n B-41012 Seville, Spain;Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37204, USA;Department of Computer Languages and Systems University of Seville, Avda. de la Reina Mercedes, s/n B-41012 Seville, Spain

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2010

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Abstract

Software product-lines (SPLs) are software platforms that can be readily reconfigured for different project requirements. A key part of an SPL is a model that captures the rules for reconfiguring the software. SPLs commonly use feature models to capture SPL configuration rules. Each SPL configuration is represented as a selection of features from the feature model. Invalid SPL configurations can be created due to feature conflicts introduced via staged or parallel configuration or changes to the constraints in a feature model. When invalid configurations are created, a method is needed to automate the diagnosis of the errors and repair the feature selections. This paper provides two contributions to research on automated configuration of SPLs. First, it shows how configurations and feature models can be transformed into constraint satisfaction problems to automatically diagnose errors and repair invalid feature selections. Second, it presents empirical results from diagnosing configuration errors in feature models ranging in size from 100 to 5,000 features. The results of our experiments show that our CSP-based diagnostic technique can scale up to models with thousands of features.