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Going deeper with optimal software products selection using many-objective optimization and satisfiability solvers

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Abstract

In search-based software engineering, one actively studied problem is the optimal software product selection from a feature model using multiple (usually more than three) optimization objectives simultaneously. This can be represented as a many-objective optimization problem. The primary goal of solving this problem is to search for diverse and high-quality valid products as rapidly as possible. Previous studies have shown that combining search-based techniques with satisfiability (SAT) solvers was promising for achieving this goal, but it remained open that how different solvers affect the performance of a search algorithm, and that whether the ways to randomize solutions in the solvers make a difference. Moreover, we may need further investigation on the necessity of mixing different types of SAT solving techniques. In this paper, we address the above open research questions by performing a series of empirical studies on 21 features models, most of which are reverse-engineered from industrial software product lines. We examine four conflict-driven clause learning solvers, two stochastic local search solvers, and two different ways to randomize solutions. Experimental results suggest that the performance can be indeed affected by different SAT solvers, and by the ways to randomize solutions in the solvers. This study serves as a practical guideline for choosing and tuning SAT solvers for the many-objective optimal software product selection problem.

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Notes

  1. In evolutionary multi-objective (EMO) community (Li et al. 2015b; Ishibuchi et al. 2017; Xiang et al. 2017a, b), the term “many-objective” refers in particular to four or more objectives.

  2. SAT4J website: http://www.sat4j.org/

  3. (P)lingeling website: http://fmv.jku.at/lingeling/

  4. Glucose website: http://www.labri.fr/perso/lsimon/glucose/

  5. Kodkod is available at http://emina.github.io/kodkod/.

  6. According to Balint and Schöning (2012), several functions can be defined for f(v). In this study, we choose the simple and effective polynomial function as shown in (2).

  7. The LVAT repository can be found at http://code.google.com/p/linux-variability-analysis-tools

  8. The codes of SATVaEA can be downloaded from https://www.researchgate.net/profile/Xiang_Yi9/publications.

  9. The G*Power is available at http://www.gpower.hhu.de/

  10. The original SATVaEA (Xiang et al. 2018) also uses SAT4J with permutated parameters, therefore we keep the name of the algorithm unchanged.

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Acknowledgments

This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant x2rjD2190840, in part by the National Natural Science Foundation of China under Grant 61773410, Grant 61673403, Grant 61703183 and Grant 61876207, in part by the Guangdong Natural Science Funds for Distinguished Young Scholar under Grant 2014A030306050, in part by the Guangdong High-Level Personnel of Special Support Program under Grant 2014TQ01X664, in part by the International Cooperation Project of Guangzhou under Grant 201807010047, in part by the Science and Technology Program of Guangzhou under Grant 201607010069, Grant 201802010007 and Grant 201804010276, in part by Guangdong Province Key Area R&D Program under Grant 2018B010109003, and in part by the Science Technique Department of Guizhou Province under Grant [2019]1164.

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, 510006, People’s Republic of China

    Yi Xiang, Xiaowei Yang & Han Huang

  2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, People’s Republic of China

    Yuren Zhou & Zibin Zheng

  3. Centre of Excellence for Research in Computational Intelligence and Applications, School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

    Miqing Li

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  1. Yi Xiang
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Xiang, Y., Yang, X., Zhou, Y. et al. Going deeper with optimal software products selection using many-objective optimization and satisfiability solvers. Empir Software Eng 25, 591–626 (2020). https://doi.org/10.1007/s10664-019-09761-2

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