Advertisement

Property-Driven Runtime Resolution of Feature Interactions

  • Santhana Gopalan Raghavan
  • Kosuke Watanabe
  • Eunsuk KangEmail author
  • Chung-Wei Lin
  • Zhihao Jiang
  • Shinichi Shiraishi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11237)

Abstract

The feature interaction problem occurs when two or more features interact and possibly conflict with each other in unexpected ways, resulting in undesirable system behaviors. Common approaches to resolving feature interactions are based on priorities, which are ineffective in scenarios where the set of features may evolve past the design phase, and where desirability of features may change dynamically depending on the state of the environment. This paper introduces a property-driven approach to feature-interaction resolution, where a desired system property is leveraged to determine which feature action should be enabled at a given context. Compared to existing approaches, our approach is capable of (1) providing resolutions even if the system evolves with new or modified features, and (2) handling complex resolution scenarios where the preference of one feature over the others may change dynamically. We demonstrate the effectiveness of our approach through a case study involving resolution of safety-critical features in an intelligent vehicle.

References

  1. 1.
    Arechiga, N., Dathathri, S., Vernekar, S., Kathare, N., Gao, S., Shiraishi, S.: Osiris: a tool for abstraction and verification of control software with lookup tables. In: Proceedings of the 1st International Workshop on Safe Control of Connected and Autonomous Vehicles, SCAV@CPSWeek 2017, Pittsburgh, PA, USA, 21 April 2017, pp. 11–18 (2017)Google Scholar
  2. 2.
    Bocovich, C., Atlee, J.M.: Variable-specific resolutions for feature interactions. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, (FSE-22), Hong Kong, China, 16–22 November 2014, pp. 553–563 (2014)Google Scholar
  3. 3.
    Calder, M., Kolberg, M., Magill, E.H., Reiff-Marganiec, S.: Feature interaction: a critical review and considered forecast. Comput. Netw. 41(1), 115–141 (2003)CrossRefGoogle Scholar
  4. 4.
    Chavan, A., Yang, L., Ramachandran, K., Leung, W.H.: Resolving feature interaction with precedence lists in the feature language extensions. In: Feature Interactions in Software and Communication Systems IX, International Conference on Feature Interactions in Software and Communication Systems, ICFI 2007, Grenoble, France, 3–5 September 2007, pp. 114–128 (2007)Google Scholar
  5. 5.
    Chen, Y., Lafortune, S., Lin, F.: Resolving feature interactions using modular supervisory control with priorities. In: Feature Interactions in Telecommunications Networks IV, Montréal, Canada, 17–19 June 1997, pp. 108–122 (1997)Google Scholar
  6. 6.
    Deshmukh, J.V., Donzé, A., Ghosh, S., Jin, X., Juniwal, G., Seshia, S.A.: Robust online monitoring of signal temporal logic. Formal Methods Syst. Des. 51(1), 5–30 (2017)CrossRefGoogle Scholar
  7. 7.
    Dokhanchi, A., Hoxha, B., Fainekos, G.: On-line monitoring for temporal logic robustness. In: Bonakdarpour, B., Smolka, S.A. (eds.) RV 2014. LNCS, vol. 8734, pp. 231–246. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11164-3_19CrossRefGoogle Scholar
  8. 8.
    Dominguez, A.L.J., Day, N.A., Joyce, J.J.: Modelling feature interactions in the automotive domain. In: International Workshop on Modeling in Software Engineering (MiSE), pp. 45–50 (2008)Google Scholar
  9. 9.
    Donzé, A., Ferrère, T., Maler, O.: Efficient robust monitoring for STL. In: Sharygina, N., Veith, H. (eds.) CAV 2013. LNCS, vol. 8044, pp. 264–279. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-39799-8_19CrossRefGoogle Scholar
  10. 10.
    Donzé, A., Maler, O.: Robust satisfaction of temporal logic over real-valued signals. In: Chatterjee, K., Henzinger, T.A. (eds.) FORMATS 2010. LNCS, vol. 6246, pp. 92–106. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15297-9_9CrossRefzbMATHGoogle Scholar
  11. 11.
    Fainekos, G.E., Pappas, G.J.: Robustness of temporal logic specifications. In: Havelund, K., Núñez, M., Roşu, G., Wolff, B. (eds.) FATES/RV -2006. LNCS, vol. 4262, pp. 178–192. Springer, Heidelberg (2006).  https://doi.org/10.1007/11940197_12CrossRefGoogle Scholar
  12. 12.
    Griffeth, N.D., Velthuijsen, H.: The negotiating agents approach to runtime feature interaction resolution. In: Feature Interactions in Telecommunications Systems, Amsterdam, The Netherlands, 8–10 May 1994, pp. 217–235 (1994)Google Scholar
  13. 13.
    Hay, J.D., Atlee, J.M.: Composing features and resolving interactions. In: ACM SIGSOFT Symposium on Foundations of Software Engineering, Proceedings, San Diego, California, USA, 6–10 November 2000, pp. 110–119 (2000)Google Scholar
  14. 14.
    Li, J., Nuzzo, P., Sangiovanni-Vincentelli, A.L., Xi, Y., Li, D.: Stochastic contracts for cyber-physical system design under probabilistic requirements. In: Proceedings of the 15th ACM-IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2017, Vienna, Austria, 29 September–02 October 2017, pp. 5–14 (2017)Google Scholar
  15. 15.
    Maler, O., Nickovic, D.: Monitoring temporal properties of continuous signals. Formal Techniques. Modelling and Analysis of Timed and Fault-Tolerant Systems, pp. 152–166. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-30206-3_12CrossRefGoogle Scholar
  16. 16.
    Metzger, A.: Feature interactions in embedded control systems. Comput. Netw. 45(5), 625–644 (2004)CrossRefGoogle Scholar
  17. 17.
    Nakamura, M., Igaki, H., Yoshimura, Y., Ikegami, K.: Considering online feature interaction detection and resolution for integrated services in home network system. In: ICFI, pp. 191–206. IOS Press (2009)Google Scholar
  18. 18.
    Parnas, D.L., Madey, J.: Functional documents for computer systems. Sci. Comput. Program. 25(1), 41–61 (1995)CrossRefGoogle Scholar
  19. 19.
    Pinisetty, S., Roop, P.S., Smyth, S., Tripakis, S., von Hanxleden, R.: Runtime enforcement of reactive systems using synchronous enforcers. In: Proceedings of the 24th ACM SIGSOFT International SPIN Symposium on Model Checking of Software, Santa Barbara, CA, USA, 10–14 July 2017, pp. 80–89 (2017)Google Scholar
  20. 20.
    Pnueli, A.: The temporal logic of programs. In: Symposium on Foundations of Computer Science, SFCS 1977, pp. 46–57 (1977)Google Scholar
  21. 21.
    Ross, S., Pineau, J., Paquet, S., Chaib-draa, B.: Online planning algorithms for POMDPs. J. Artif. Intell. Res. 32, 663–704 (2008)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Seuken, S., Zilberstein, S.: Formal models and algorithms for decentralized decision making under uncertainty. Auton. Agent. Multi-Agent Syst. 17(2), 190–250 (2008)CrossRefGoogle Scholar
  23. 23.
    Sundström, C., Frisk, E., Nielsen, L.: Diagnostic method combining the lookup tables and fault models applied on a hybrid electric vehicle. IEEE Trans. Control Syst. Technol. 24(3), 1109–1117 (2016)CrossRefGoogle Scholar
  24. 24.
    Tsang, S., Magill, E.H.: The network operator’s perspective: detecting and resolving feature interaction problems. Comput. Netw. 30(15), 1421–1441 (1998)Google Scholar
  25. 25.
    Wu, M., Zeng, H., Wang, C., Yu, H.: Safety guard: runtime enforcement for safety-critical cyber-physical systems: invited. In: Proceedings of the 54th Annual Design Automation Conference, DAC 2017, Austin, TX, USA, June 18–22 2017, pp. 84:1–84:6 (2017)Google Scholar
  26. 26.
    Yarosh, L., Zave, P.: Locked or not?: Mental models of IoT feature interaction. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, USA, 06–11 May 2017, pp. 2993–2997 (2017)Google Scholar
  27. 27.
    Zibaeenejad, M.H., Zhang, C., Atlee, J.M.: Continuous variable-specific resolutions of feature interactions. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2017, Paderborn, Germany, 4–8 September 2017, pp. 408–418 (2017)Google Scholar
  28. 28.
    Zimmer, P.A., Atlee, J.M.: Ordering features by category. J. Syst. Softw. 85(8), 1782–1800 (2012).  https://doi.org/10.1016/j.jss.2012.03.025CrossRefGoogle Scholar
  29. 29.
    Zurbriggen, F., Ott, T., Onder, C.H.: Fast and robust adaptation of lookup tables in internal combustion engines: feedback and feedforward controllers designed independently. Proc. Inst. Mech. Eng. Part D: J. Automob. Eng. 230(6), 723–735 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Santhana Gopalan Raghavan
    • 1
  • Kosuke Watanabe
    • 2
  • Eunsuk Kang
    • 3
    Email author
  • Chung-Wei Lin
    • 4
  • Zhihao Jiang
    • 5
  • Shinichi Shiraishi
    • 2
  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.Toyota InfoTechnology CenterMountain ViewUSA
  3. 3.Carnegie Mellon UniversityPittsburghUSA
  4. 4.National Taiwan UniversityTaipeiTaiwan
  5. 5.ShanghaiTech UniversityShanghaiChina

Personalised recommendations