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Verification of SPL Feature Model by using Bayesian Network


  • Depatment of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
  • Depatment of Computer Science and Engineering, East West University, Dhaka, India


Feature Tree represents all the features along with their relationship of a Software Product Line. Any defect in feature model can diminish the benefits of product line approach. Hence, the analysis of feature model plays a key role towards the success of any Software Product Line. This paper presents various analysis rules for cardinality-based feature model of both dead and false optional features. These rules are then verified by using Bayesian Network Based inference mechanism. Such verification not only confirms the analysis rules of the feature trees but also ensures the applicability of probabilistic information into the feature trees.


Bayesian Network, Dead Feature, False Optional Feature, Feature Analysis, Software Product Line.

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  • Nyholm C. Product line development– An overview. Building Reliable Component- Based Systems. Extended Report for Crnkovic I, Larsson M, editors. Artech House; 2002 Jul. p. 44–58.
  • Somerville I. Software Reuse. Software Engineering. 9th ed. Pearson; 2010.
  • Clements PC, Northrop LM. Software product lines: Practices and patterns. SEI Series in Software Engineering. Addison-Wesley; 2001.
  • Czarnecki K, Helsen S, Eisenecker U. Formalizing cardinality-based feature models and their specialization. Software Process: Improvement and Practice. 2005 Jan/Mar; 10(1):7–29.
  • Riebisch M, Bollert K, Streitferdt D, Philippow I. Extending feature diagrams with UML multiplicities. Proceedings of the 6th World Conference on Integrated Design and Process Technology (IDPT2002); 2002 Jun.
  • Fenton N, Neil M, Marquez D. Using Bayesian networks to predict software defects and reliability. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2008 Dec; 222(4):701–12.
  • Kang KC, Cohen SG, Hess J, Novak W, Peterson A. Feature-Oriented Domain Analysis (FODA) feasibility study. Technical Report. Carnegie-Mellon University: Software Engineering Institute; 1990 Nov.
  • Benavides D, Felfernig A, Jose A, Reinfrank F. Automated analysis in feature modelling and product configuration. Proceedings of 13th International Conference on Software Reuse, ICSR; Pisa. 2013 Jun. p. 160–75.
  • Benavides D, Segura S, Ruiz-Cortes A. Automated analysis of feature models 20 years later: A literature review. Information Systems. 2010 Sep; 35(6):615–36.
  • Czarnecki K, Bednasch T, Unger P, Eisenecker U. Generative programming for embedded software: An industrial experience report. Proceedings of the 1st ACM SIGPLAN/SIGSOFT Conference on Generative Programming and Component Engineering; 2002. p. 156–72.
  • Batory D, Benavides D, Ruiz-Cortes A. Automated analysis of feature models: Challenges ahead. Communications of the ACM. 2006 Dec; 49(12):45–7.
  • Trinidad P, Benavides D, Duran A, Ruiz-Cortez A, Toro M. Automated error analysis for the agilization of feature modeling. Journal of Systems and Software. 2008 Jun; 81(6):883–96.
  • White J, Benavides D, Schmidt DC, Trinidad P, Dougherty B, Ruiz-Cortes A. Automated diagnosis of feature model configurations. Journal of Systems and Software. 2010 Jul; 83(7):1094–107.
  • Massen T, Lichter H. Deficiencies in feature models. Workshop on Software Variability Management for Product Derivation- Towards Tool Support; 2004.
  • Ripon S, Hossain SJ, Azad K, Hassan M. Modeling and analysis of product line variants. Proceedings of SPLC; 2012 Sept. p. 26–31.
  • Rahman M, Ripon S. Using bayesian networks to model and analyze software product line feature model. Multi-disciplinary trends in artificial intelligence. Murty MN, Xiangjian H, Chillarige RR, editors. Lecture Notes in Computer Science. Springer International Pub; 2014. p. 220–31.
  • Elfaki A, Fong S, Vijayaprasad P, Johar M, Fadhil M. Using rule-based method for detecting anomalies in software product line. Research Journal of Applied Sciences, Engineering and Technology. 2014; 7(2):275–81.
  • Ripon S, Piash MM, Hossain SMA, Uddin MS. Modeling product line variants – semantic web approach. Lecture Notes on Software Engineering. 2013 Feb; 1(1):84–8.
  • Ripon S, Piash MM, Hossain SMA, Uddin MS. Semantic web based analysis of product line variant model. International Journal of Computer and Electrical Engineering. 2014 Feb; 6(1):1–6.
  • Rincon LF, Giraldo GL, Mazo R, Salinesi C. An ontological rule-based approach for analyzing dead and false optional features in feature models. Electronic Notes in Theoretical Computer Science. 2014 Feb; 302:111–132.


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