Saeideh Hamidi
ABSTRACT
As modern software systems grow in size and complexity so do their configuration possibilities. Users are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. We propose a method to construct adaptive configuration elicitation dialogs through utilizing crowd wisdom. A set of configuration preferences in the form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Process (MDP). Association rules are used to inform the model about configuration decisions that can be automatically inferred from knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. The method is applied to the privacy configuration of Facebook.
- R. Agrawal, T. ImieliŃski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (SIGMOD'93), 1993. Google Scholar
Digital Library
- X. Amatriain, A. Jaimes, N. Oliver, and J. M. Pujol. Data mining methods for recommender systems. In Recommender Systems Handbook, pages 39--71. Springer, 2011.Google ScholarCross Ref
- C. Boutilier, T. Dean, and S. Hanks. Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research, 1999.Google ScholarCross Ref
- C. Boutilier, R. Patrascu, P. Poupart, and D. Schuurmans. Constraint-based optimization and utility elicitation using the minimax decision criterion. Artificial Intelligence, 170(8--9):686--713, 2006. Google ScholarDigital Library
- S. Chen and M. Erwig. Optimizing the product derivation process. In Proc. of the 15th International Software Product Line Conference (SPLC'11), pages 35--44, 2011. Google ScholarDigital Library
- D. Dhungana, P. Grünbacher, and R. Rabiser. DecisionKing: A exible and extensible tool for integrated variability modeling. In Proc. of the 1st International Workshop on Variability Modelling of Software-intensive Systems (VAMoS'07), pages 119--128, 2007.Google Scholar
- J. Hipp, U. Güntzer, and G. Nakhaeizadeh. Algorithms for association rule mining|a general survey and comparison. ACM SIGKDD Explorations Newsletter, 2(1):58--64, 2000. Google ScholarDigital Library
- B. Hui, S. Liaskos, and J. Mylopoulos. Requirements analysis for customizable software: a goals-skills-preferences framework. In Proc. of the 11th IEEE Requirements Engineering Conference, pages 117--126, 2003. Google ScholarDigital Library
- M. Janota, G. Botterweck, and J. Marques-Silva. On lazy and eager interactive reconfiguration. In Proc. of the 8th International Workshop on Variability Modelling of Software-Intensive Systems (VaMoS'14), pages 81--88, 2013. Google ScholarDigital Library
- N. Kushman and D. Katabi. Enabling configuration-independent automation by non-expert users. In Proc. of the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2010), pages 223--236, 2010. Google ScholarDigital Library
- S. Liaskos, S. M. Khan, M. Litoiu, M. D. Jungblut, V. Rogozhkin, and J. Mylopoulos. Behavioral adaptation of information systems through goal models. Information Systems, 37(8):767--783, 2012. Google ScholarDigital Library
- S. Liaskos, A. Lapouchnian, Y. Wang, Y. Yu, and S. Easterbrook. Configuring common personal software: a requirements-driven approach. In Proc. of the 13th IEEE International Requirements Engineering Conference (RE'05), pages 9--18, 2005. Google ScholarDigital Library
- S. Liaskos, S. A. McIlraith, S. Sohrabi, and J. Mylopoulos. Representing and reasoning about preferences in requirements engineering. Requirements Engineering, 16(3):227--249, Sept. 2011. Google ScholarDigital Library
- W. E. Mackay. Triggers and barriers to customizing software. In Proc. of the SIGCHI Conference on Human Factors in Computing Systems (CHI'91), pages 153--160, 1991. Google ScholarDigital Library
- J. Mylopoulos, L. Chung, S. Liao, H. Wang, and E. Yu. Exploring alternatives during requirements analysis. IEEE Software, 18(1):92--96, Jan 2001. Google ScholarDigital Library
- A. Nohrer and A. Egyed. Optimizing user guidance during decision-making. In Proc. of the 15th International Software Product Line Conference (SPLC'11), pages 25--34, 2011. Google ScholarDigital Library
- J. J. Sandvig, B. Mobasher, and R. Burke. Robustness of collaborative recommendation based on association rule mining. In Proc. of the 2007 ACM Conference on Recommender Systems (RecSys'07), pages 105--112, 2007. Google ScholarDigital Library
- K. Schmid, R. Rabiser, and P. Grünbacher. A comparison of decision modeling approaches in product lines. In Proc. of the 5th Workshop on Variability Modeling of Software-Intensive Systems (VaMoS'11), pages 119--126, 2011. Google ScholarDigital Library
- M. Stolze and M. Ströbel. Utility-based decision tree optimization: A framework for adaptive interviewing. In User Modeling 2001, volume LNCS 2109, pages 105--116. 2001. Google ScholarDigital Library
- H. J. Wang, J. C. Platt, Y. Chen, R. Zhang, and Y.-M. Wang. Automatic misconfiguration troubleshooting with PeerPressure. In Proc. of the 6th Symposium on Operating System Design and Implementation (OSDI 2004), pages 245--258, 2004. Google ScholarDigital Library
- J. White, B. Dougherty, D. C. Schmidt, and D. Benavides. Automated reasoning for multi-step feature model configuration problems. In Proc. of the 13th International Software Product Line Conference (SPLC '09), pages 11--20, 2009. Google ScholarDigital Library
Index Terms
- Constructing adaptive configuration dialogs using crowd data
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