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Analyzing Inter-objective Relationships: A Case Study of Software Upgradability

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9921)


In the process of solving real-world multi-objective problems, many existing studies only consider aggregate formulations of the problem, leaving the relationships between different objectives less visited. In this study, taking the software upgradability problem as a case study, we intend to gain insights into the inter-objective relationships of multi-objective problems. First, we obtain the Pareto schemes by uniformly sampling a set of solutions within the Pareto front. Second, we analyze the characteristics of the Pareto scheme, which reveal the relationships between different objectives. Third, to estimate the inter-objective relationships for new upgrade requests, we build a predictive model, with a set of problem-specific features. Finally, we propose a reference based indicator, to assess the risk of applying single-objective algorithms to solve the multi-objective software upgradability problem. Extensive experimental results demonstrate that, the predictive models built with problem-specific features are able to predict both algorithm independent inter-objective relationships, as well as the algorithm performance specific indicator properly.


  • Pareto front
  • Meta-learning
  • Empirical analysis

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  • DOI: 10.1007/978-3-319-45823-6_41
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    Note that these requests could be detected by the feature extraction phase, see RQ2.

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  1. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    MathSciNet  CrossRef  MATH  Google Scholar 

  2. Corne, D.W., Knowles, J.D.: Techniques for highly multiobjective optimisation: some nondominated points are better than others. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 773–780. ACM (2007)

    Google Scholar 

  3. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comp. 18(4), 577–601 (2014)

    CrossRef  Google Scholar 

  4. Gebser, M., Kaminski, R., Schaub, T.: Aspcud: a linux package configuration tool based on answer set programming. In: Proceedings of Workshop on Logics for Component Configuration 2010 (2010)

    Google Scholar 

  5. Hutter, F., Xu, L., Hoos, H.H., Leyton-Brown, K.: Algorithm runtime prediction: methods & evaluation. Artif. Intell. 206, 79–111 (2014)

    MathSciNet  CrossRef  MATH  Google Scholar 

  6. Ignatiev, A., Janota, M., Marques-Silva, J.: Towards efficient optimization in package management systems. In: Proceedings of the 36th International Conference on Software Engineering, pp. 745–755 (2014)

    Google Scholar 

  7. Lindauer, M., Hoos, H.H., Hutter, F., Schaub, T.: Autofolio: an automatically configured algorithm selector. J. Artif. Intell. Res. 53, 745–778 (2015)

    MathSciNet  Google Scholar 

  8. Liu, Z., Yan, Y., Qu, X., Zhang, Y.: Bus stop-skipping scheme with random travel time. Transp. Res. Part C Emerg. Technol. 35, 46–56 (2013)

    CrossRef  Google Scholar 

  9. Lokman, B., Köksalan, M.: Finding highly preferred points for multi-objective integer programs. IIE Trans. 46(11), 1181–1195 (2014)

    CrossRef  Google Scholar 

  10. Michel, C., Rueher, M.: Handling software upgradeability problems with MILP solvers. In: Proceedings of Workshop on Logics for Component Configuration 2010, vol. 29, pp. 1–10 (2010)

    Google Scholar 

  11. Trezentos, P., Lynce, I., Oliveira, A.L.: Apt-pbo: solving the software dependency problem using pseudo-boolean optimization. In: Proceedings of the IEEE/ACM International Conference on Automated Software Engineering, pp. 427–436 (2010)

    Google Scholar 

  12. Verel, S., Liefooghe, A., Jourdan, L., Dhaenens, C.: Analyzing the effect of objective correlation on the efficient set of MNK-landscapes. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 116–130. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  13. Wang, H., Yao, X.: Objective reduction based on nonlinear correlation information entropy. Soft Comput. 20(6), 2393–2407 (2016)

    CrossRef  Google Scholar 

  14. Xuan, J., Martinez, M., DeMarco, F., Clement, M., Marcote, S.L., Durieux, T., Berre, D.L., Monperrus, M.: Nopol: automatic repair of conditional statement bugs in java programs. IEEE Trans. Software Eng. (2016, online)

    Google Scholar 

  15. Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comp. 11(6), 712–731 (2007)

    CrossRef  Google Scholar 

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This work is supported in part by the National Natural Science Foundation of China under Grants 61370144 and 61403057, in part by National Program on Key Basic Research Project under Grant 2013CB035906, and in part by the Fundamental Research Funds for the Central Universities under Grants DUT15TD37 and DUT16RC(4)62.

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Correspondence to He Jiang .

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Ren, Z., Jiang, H., Xuan, J., Tang, K., Hu, Y. (2016). Analyzing Inter-objective Relationships: A Case Study of Software Upgradability. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham.

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