1. Loc-based Variability for Mobile Information Systems Raian Ali, Fabiano Dalpiaz, Paolo Giorgini CAiSE’08 18-20 June 2008

2. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Talk outline Location-based MobIS Limits of existing modeling techniques ▫Context Models ▫Software Variability Models ▫Goal models Location-based goal modeling Location-based analysis Conclusions 2

3. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Location-based MobIS [Streizt et al., 2005] [Weiser, 1991] [Krogstie et al., 2004]

4. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Limits of existing modeling techniques Context models [Yau et al., 2006] [Henricksen et al., 2004] [Wang et al., 2004]

5. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Limits of existing modeling techniques Context models Several context models have been proposed. Without specifying the relation between context and its use, we cannot say ▫Why context is needed ▫Which is the relevant part of context ▫How context influences software derivation Context awareness is mainly focused on the software domain, not on the problem domain.

6. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Limits of existing modeling techniques Software variability models [Kang et al., 1998] [Pohl et al., 2005]

7. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Limits of existing modeling techniques Software variability models By modeling variability, SW product line engineering creates systematically a diversity of similar products at low costs, in short time, and with high quality [Pohl et al., 2005]. To model location in MobIS we need: ▫Autonomous selection between features ▫Higher level of abstraction that justifies the features

8. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Limits of existing modeling techniques Goal models [Yu, 1995] [Bresciani et al., 2004]

9. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Limits of existing modeling techniques Goal models Goal models provide: ▫High-level goals decomposition to discover alternatives. ▫Good modeling of the problem domain ▫Higher level of abstraction justifies why software is needed. … but: ▫Goal models do not specify where an alternative is:  Applicable  Recommended

10. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Location-based goal modeling Location-based (LB) goal models contain variability points annotated with location properties: 1.LB Or-Decomposition: the basic variability construct to express alternative goal decompositions 2.LB contribution: contributions to soft-goals depends on the location. 3.LB dependency: the actor may depend on other actors in certain locations. 4.LB Goal-Activation: location changes trigger (activate, stop) goals. 5.LB And-Decomposition: not all and-decomposition sub- goals are needed in some location.

11. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Location-based goal modeling Location-based goal model Location model

12. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Location-based analysis Loc-based Goal Satisfiability (LGS) ▫Is a goal satisfiable in a certain location instance? Location Property Satisfability (LPS) ▫What a Location lacks for satisfying a Goal! Preference Analysis (PA): Preferences can be specified over softgoals [Liaskos et al., 2006] to choose when: ▫More than one alternative to satisfy a Goal in a location. ▫More than one Location modification is possible to make a goal satisfiable.

13. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Conclusions We exploit i*/Tropos goal models to model location- based MobIS ▫We associate context information to variability points ▫We support an automated derivation of loc-based software We introduce three analysis techniques ▫Loc-based goal satisfiability ▫Location property satisfiability ▫Preference based alternatives adopting

14. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Future work Finding suitable abstraction for modeling location at the social level. Looking for a suitable formalization Formalizing the whole i*/Tropos loc-based GM Positioning our proposed models into the whole MobIS SDLC. Developing different case studies taken from different domains

15. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini References (1) [Streizt et al., 2005] Streitz, N., Nixon, P.: The disappearing computer. Commun. ACM 48(3) (2005) [Weiser, 1991] Weiser, M.: The computer for the twenty-first century. Scientific American 265(3) (1991)94–104 [Krogstie et al., 2004]Krogstie, J., Lyytinen, K., Opdahl, A., Pernici, B., Siau, K., Smolander, K.: Research areas and challenges for mobile information systems. International Journal of Mobile Communications 2(3) (2004) 220–234 [Yau et al., 2006] Yau, S., Liu, J.: Hierarchical situation modeling and reasoning for pervasive computing. Proceedings of 3rd Workshop on Software Technologies for Future Embedded and Ubiquitous Systems (SEUS) (2006) 5-10 [Henricksen et al., 2004] Henricksen, K., Indulska, J.: A software engineering framework for context-aware pervasive computing. PerCom (2004) 77–86 5. [Wang et al., 2004] Wang, X.H., Zhang, D.Q., Gu, T., Pung, H.K.: Ontology based context modeling and reasoning using owl. In: PERCOMW ’04: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, Washington, DC, USA, IEEE Computer Society (2004) 18–22

16. 04/10/2015 R. Ali, F. Dalpiaz, P. Giorgini References (2) [Pohl et al., 2005] Pohl, K., Böckle, G., van der Linden, F.: Software Product Line Engineering: Foundations,Principles, and Techniques. Springer (2005) [Kang et al., 1998] Kang, K., Kim, S., Lee, J., Kim, K., Shin, E., Huh, M.: Form: A feature-oriented reuse method with domain-specific reference architectures. Annals of Software Engineering 5 (1998) 143–168 [Bresciani et al., 2004] Bresciani, P., Perini, A., Giorgini, P., Giunchiglia, F., Mylopoulos, J.: Tropos: An agent oriented software development methodology. Autonomous Agents and Multi-Agent Systems 8(3) (2004) 203–236 [Yu, 1995] Yu, E.: Modelling strategic relationships for process reengineering. Ph.D. Thesis, University of Toronto (1995) [Liaskos et al., 2006] Liaskos, S., McIlraith, S., Mylopoulos, J.: Representing and reasoning with preference requirements using goals. Technical report, Dept. of Computer Science, University of Toronto (2006) ftp://ftp.cs.toronto.edu/pub/reports/csrg/542.