Total views : 299

Multi-Objective Optimization of Feature Model in Software Product Line: Perspectives and Challenges

Affiliations

  • Department of Computer Science and Engineering, Hanyang University, South Korea

Abstract


Software Product Line (SPL) is process for developing families of software with reusability of features categorized as common and variable features. Feature Model (FM) is developed to manage these features. Common features are easy to manage, however variable features are hard to manage because of complex relations and constraints between features. Optimization is required to manage the variabilities for best selection of features and product configurations. To this end, different Multi-Objective Evolutionary Algorithms have been proposed to get the optimal solutions of feature model. In this paper we have compared among three main optimization algorithms i.e. IBEA, NSGA-II and MOCell. Our comparison is based on previous research correctness solutions for product’s configuration with five objective functions on different feature models from SPLOT and LVAT repositories. The goal of this comparison is to find the current research prospective and challenges of multi-objective optimization in FM.

Keywords

Feature Model, Optimization of Feature Model, Software Product Line.

Full Text:

 |  (PDF views: 222)

References


  • Boonon P, Muenchaisri P. An approach to clustering Feature Model based on adaptive behavior for dynamic software product line. 2014 IEEE International Conference on Information Science and Applications (ICISA); 2014 May. p. 1–4.
  • Nie K, Zhang L, Geng Z. Product line variability modeling based on model difference and merge. IEEE 36th Annual Computer Software and Applications Conference Workshops (COMPSACW); 2012 Jul. p. 509–13.
  • Zheng L, Zhang C, Wu Z, Liu M. Managing resource repository of a Software Product Line with Feature Model. IEEE International Conference on Computational Intelligence and Software Engineering, CiSE; 2009 Dec. p. 1–4.
  • Loesch F, Ploedereder E. Optimization of variability in software product lines. IEEE 11th International Software Product Line Conference (SPLC); 2007 Sep. p. 151–62.
  • Abbas A, Wu Z, Siddiqui IF, Lee SUJ. An approach for optimized feature selection in software product lines using union-find and Genetic Algorithms. Indian Journal of Science and Technology. 2016 May; 9(17):1–8.
  • Mohan K, Ramesh B, Sugumaran V. Integrating Software Product Line engineering and agile development. IEEE Software. 2010; 27:3:48–55.
  • Ripon S, Azad K, Hossain SJ, Hassan M. Modeling and analysis of product-line variants. Proceedings of the 16th International Software Product Line Conference, ACM; 2012 Sep; 2: 26–31.
  • Sinnema M, Deelstra S, Nijhuis J, Bosch J. Managing variability in software product families. Proceedings of the 2nd Groningen Workshop on Software Variability Management; 2004 Dec.
  • Holdschick H. Challenges in the evolution of model-based software product lines in the automotive domain. Proceedings of the 4th International Workshop on Feature-Oriented Software Development, ACM; 2012 Sep. p. 70–3.
  • Czarnecki K, Grunbacher P, Rabiser R, Schmid K, Wąsowski A. Cool features and tough decisions: A comparison of variability modeling approaches. Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems, ACM; 2012 Jan. p. 173–82.
  • Lee K, Kang KC, Kim M, Park S. Combining feature-oriented analysis and aspect-oriented programming for product line asset development. IEEE 10th International Software Product Line Conference (SPLC); 2006 Aug. p. 10.
  • Lee J, Kang KC. Feature binding analysis for product line component development. International Workshop on Software Product-Family Engineering. Springer Berlin Heidelberg; 2003 Nov. p. 250–60.
  • Lee SUJ. An effective methodology with automated product configuration for Software Product Line development. Mathematical Problems in Engineering. 2015; 2015: 11 pages.
  • Sinnema M, Deelstra S. Classifying variability modeling techniques. Information and Software Technology. 2007; 49(7):717–39.
  • Noorian M, Bagheri E, Du W. Capturing non-functional properties through model interlinking. IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE); 2014 May. p. 1–6.
  • Rosenmuller M, Siegmund N. Automating the configuration of multi software product lines. VaMoS. 2010; 10:123–30.
  • Korner M, Herold S, Rausch A. Composition of applications based on software product lines using architecture fragments and component sets. Proceedings of the WICSA, Companion Volume, ACM; 2014 Apr. p. 13.
  • Yamany E, Eid A, Shaheen M, Sayyad A.S. OPTI-SELECT: An interactive tool for user-in-the-loop feature selection in software product lines. 18th International Software Product Line Conference: Companion Volume for Workshops, Demonstrations and Tools, ACM. 2014 Sep; 2014:126–9.
  • Karimpour R, Ruhe G. Evolutionary robust optimization for Software Product Line scoping: An explorative study. Computer Languages, Systems and Structures; 2016.
  • Metzger A, Pohl K. Software Product Line engineering and variability management: Achievements and challenges. Proceedings of the on Future of Software Engineering; 2014 May. p. 70–84.
  • Thum T, Kastner C, Benduhn F, Meinicke J, Saake G, Leich T. Feature IDE: An extensible framework for feature-oriented software development. Science of Computer Programming. 2014; 79:70–85.
  • Benavides D, Trinidad P, Ruiz-Cortes A. Automated reasoning on feature models. International Conference on Advanced Information Systems Engineering, Springer Berlin Heidelberg; 2005 Jun. p. 491–503.
  • Wang YL, Pang JW. Ant colony optimization for feature selection in software product lines. Journal of Shanghai Jiaotong University (Science). 2014; 19:50–8.
  • Guo J, White J, Wang G, Li J, Wang Y. A Genetic Algorithm for optimized feature selection with resource constraints in software product lines. Journal of Systems and Software. 2011; 84(12):2208–21.
  • White J, Doughtery B, Schmidt DC. Filtered cartesian flattening: An approximation technique for optimally selecting features while adhering to resource constraints. SPLC (2); 2008 Oct. p. 209–16.
  • Henard C, Papadakis M, Harman M, Le Traon Y. Combining multi-objective search and constraint solving for configuring large software product lines. IEEE/ACM 37th IEEE International Conference on Software Engineering. 2015 May; 1:517–28.
  • Zitzler E, Kunzli S. Indicator-based selection in multi-objective search. International Conference on Parallel Problem Solving from Nature, Springer Berlin Heidelberg; 2004 Sep. p. 832–42.
  • Strickler A, Lima JAP, Vergilio SR, Pozo AT. Deriving products for variability test of Feature Models with a hyper-heuristic approach. Applied Soft Computing, 2016 Dec; 49:1232–42.
  • Sayyad AS, Goseva-Popstojanova K, Menzies T, Ammar H. On parameter tuning in search based software engineering: A replicated empirical study. IEEE 3rd International Workshop Replication in Empirical Software Engineering Research (RESER); 2013 Oct. p. 84–90.
  • Nebro AJ, Durillo JJ, Luna F, Dorronsoro B, Mocell AE: A cellular Genetic Algorithm for multi-objective optimization. International Journal of Intelligent Systems. 2009; 24(7):726–46.
  • Sayyad AS, Menzies T, Ammar H. On the value of user preferences in search-based software engineering: A case study in software product lines. IEEE 35th International Conference on Software Engineering (ICSE); 2013 May. p. 492–501.
  • Sayyad AS, Ingram J, Menzies T, Ammar H. Optimum feature selection in software product lines: Let your model and values guide your search. IEEE 1st International Workshop on Combining Modeling and Search-Based Software Engineering (CMSBSE); 2013 May. p. 22–7.
  • Xue Y, Zhong J, Tan TH, Liu Y, Cai W, Chen M, Sun J. IBED: Combining IBEA and DE for optimal feature selection in Software Product Line engineering. Applied Soft Computing, 2016 Dec; 49:1215–31.
  • Tan TH, Xue Y, Chen M, Sun J, Liu Y, Dong JS. Optimizing selection of competing features via feedback-directed evolutionary algorithms. International Symposium on Software Testing and Analysis, ACM; 2015 Jul. p. 246–56.
  • Sayyad AS, Ingram J, Menzies T, Ammar H. Scalable product line configuration: A straw to break the camel's back. IEEE/ACM 28th International Conference on Automated Software Engineering (ASE); 2013 Nov. p. 465–74.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.