Location-based Variability for Mobile Information Systems

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

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

3. Location-based MobIS R. Ali, F. Dalpiaz, P. Giorgini Research question: How can location-based MobIS be modeled?

4. Limits of existing modeling techniquesContext models Several context models have been proposed Ontology-based [Yau et al., 2006] [Wang et al., 2004] Object-based [Henricksen et al., 2004] They don’t specify the relation between context and its use Why is context needed? Which is the relevant part of context? Context awareness is mainly focused on the software domain, not on the problem domain. R. Ali, F. Dalpiaz, P. Giorgini

5. Limits of existing modeling techniquesSoftware variability models 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-based MobIS we need: Autonomous selection between features Higher level of abstraction that justifies the features R. Ali, F. Dalpiaz, P. Giorgini Feature models [Kang et al., 1998]

6. Limits of existing modeling techniquesGoal models [Yu, 1995] [Bresciani et al., 2004] R. Ali, F. Dalpiaz, P. Giorgini

7. Limits of existing modeling techniquesGoal 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. Goal models do not specify where an alternative is: Applicable Recommended We propose Location-based goal modeling Location is defined from an actor’s perspective R. Ali, F. Dalpiaz, P. Giorgini

8. Location-based goal modeling Location-based (LB) goal models contain variation points annotated with location properties: LB Or-Decomposition: the basic variability construct to express alternative goal decompositions LB contribution: contributions to softgoals is location-based R. Ali, F. Dalpiaz, P. Giorgini L7: high expertise in using PDA, PDA has touch screen L1: accessible wireless network exists, PDA supports wireless conn. L8: low expertise in using PDA, No PDA touch screen L4: Cable-based Net. terminals exists, PDA supports cable conn.

9. Location-based goal modeling LB dependency: the actor may depend on other actors in certain locations. LB Goal-Activation: location changes trigger (activate, stop) goals. R. Ali, F. Dalpiaz, P. Giorgini 02/10/2014 L19: client is close to a product with a special offer, product can be intersting to client L18: the mall website has a mobile devices version

10. Location-based goal modeling LB And-Decomposition: not all and-decomposition sub-goals are needed in some location. R. Ali, F. Dalpiaz, P. Giorgini 02/10/2014 L5: the client uses the system for the first time

11. Location-based goal modeling R. Ali, F. Dalpiaz, P. Giorgini Location-based goal model Location model

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

13. Conclusions and Future work Conclusions: Extension of i*/Tropos goal models to model location-based MobIS We associate location properties to goal model variation points We support a step towards an automated derivation of MobIS Proposal of three analysis techniques Future work: Finding suitable abstraction for modeling location at the social level. Looking for a suitable formalization language (currently, Datalog) Define a methodology for the development of location-based MobIS Evaluation of the approach R. Ali, F. Dalpiaz, P. Giorgini

14. Thank you! Questions? R. Ali, F. Dalpiaz, P. Giorgini

15. R. Ali, F. Dalpiaz, P. Giorgini [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 [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 References (1)

16. References (2) [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. R. Ali, F. Dalpiaz, P. Giorgini