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

Loc-based Variability for Mobile Information Systems

Raian Ali, Fabiano Dalpiaz, Paolo Giorgini

CAiSE’0818-20 June 2008

19/04/23R. 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


Location-based MobIS

[Streizt et al., 2005] [Weiser, 1991] [Krogstie et al., 2004]


Limits of existing modeling techniquesContext models

[Yau et al., 2006][Henricksen et al., 2004]

[Wang et al., 2004]


Limits of existing modeling techniquesContext 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.


Limits of existing modeling techniquesSoftware variability models

[Kang et al., 1998] [Pohl et al., 2005]


Limits of existing modeling techniquesSoftware 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


Limits of existing modeling techniquesGoal models

[Yu, 1995] [Bresciani et al., 2004]


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.• … but:

▫ Goal models do not specify where an alternative is: Applicable Recommended


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 decompositions2.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.


Location-based goal modeling

Location-based goal model Location model


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.


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


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


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


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.

Documents

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