Raian Ali, Fabiano Dalpiaz , Paolo Giorgini

1. Location-based Software Modeling and Analysis: Tropos-based Approach Raian Ali, Fabiano Dalpiaz, Paolo Giorgini

2. 2 2 R. Ali, F. Dalpiaz, P. Giorgini Talk outline • Limits of existing modeling techniques • Location-based Software • Modeling challenges • Features to support • Tropos and location-based SW • Advantages and drawbacks of Tropos • Location-based Tropos • Location-based Tropos process • Location-based analysis • Conclusions

3. Research question 3 R. Ali, F. Dalpiaz, P. Giorgini • The concept of location is becoming more and more important (e.g. Ubiquitous computing, AmI) • Location-based software is characterized by its ability to • Reason about the surrounding location • Adapt autonomously its behavior to be location compliant What and How to model and analyzelocation-based SW?

4. Limits of existing models: context 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. 4 R. Ali, F. Dalpiaz, P. Giorgini

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

6. Location modeling constructs What is the conceptual framework? Location relevancy What should be modeled? Location rules Constraints of the specific location Location-based behavior Different behaviors are enabled/disabled depending on the current location Location-based SW: modeling challenges

7. Hierarchical behaviors construction Avoid “one location, one behavior” cases Location-based behavior evaluation Payoff functions to evaluate alternatives Choice can be location-dependent Location-based SW: modeling challenges

8. Location-based SW: features to support Location identification Instantiate a location model Location-based behavior adaptation Select the bestpossible behavior to achieve the goals Location-based information processing Information request Relevant information extraction Information delivery

9. Location-based SW: features to support Act on behalf of users Location-based SW represents the userwheninteractingwithother location actors Personalization Eachuserhas a profile and preferences

10. Tropos for location-based SW: goal models 10 R. Ali, F. Dalpiaz, P. Giorgini 22/10/2014

11. Tropos for location-based SW: benefits Goal models provide: High-level goals decomposition to discover alternatives. Modeling of the problem domain High level of abstraction that justifies why software is needed. Modeling of location at the social level (dependencies) 11 R. Ali, F. Dalpiaz, P. Giorgini 22/10/2014

12. Tropos for location-based SW: limits The actors network is static Location is dynamic Actor/Resource modeling is limited: no means to express Availability Constraints on dependencies More actors able to fulfill the same goal No specification of where an alternative is: Applicable / Forbidden Recommended 12 R. Ali, F. Dalpiaz, P. Giorgini 22/10/2014 Our solution: Location-based Tropos

13. Location-based Tropos 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 13 R. Ali, F. Dalpiaz, P. Giorgini L1: a terminal is free, has a language in common with the passenger, ... L2: the railway station has a wireless network and passenger’s PDA support WiFi, ... L4: low expertise in using PDA, No PDA touch screen. L3: good expertise in using PDAs and PDA has touch screen

14. Location-based Tropos LB dependency: the actor may depend on other actors in certain locations. LB Goal-Activation: location triggers goals. 14 R. Ali, F. Dalpiaz, P. Giorgini 22/10/2014 L6: the assistant is idle, has a language in common with the requesting passenger, ... L5: the web-site enables payment with the customer credit card’s type

15. Location-based Tropos LB And-Decomposition: not all and-decomposition sub-goals are needed in some location. 15 R. Ali, F. Dalpiaz, P. Giorgini 22/10/2014 L7: the passenger is not familiar with terminals

16. Location-based Tropos process Model the social structure of a location class Actors and dependencies Identify mobile actors Those actors that need location-based SW Assign a system-to-be actor to each mobile actor Use goal analysis to define the rationale Identify the variation points Assign location properties to variation points Derive a location model from location properties

17. Location-based Tropos 17 R. Ali, F. Dalpiaz, P. Giorgini Location-based goal model Location model

18. Location-based analysis Location model and Location Properties have been formalized using Datalog¬ Location properties satisfiability have been tested using DLV Solver. An instance of the location model implies a set of goal satisfaction alternatives.

19. Location-based analysis Location-based Goal Satisfiability (LGS) Is a goal satisfiable in a certain location? 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. 19 R. Ali, F. Dalpiaz, P. Giorgini

20. Conclusions and Future work Conclusions We have shown particularity and importance of modeling location variability in location-based SW We addressed some conceptual modeling challenges Modifying and extending Tropos We defined three formal analysis techniques Future work Refine the modeling framework Choose an expressive enough formal language Evaluate on a real-world case study 20 R. Ali, F. Dalpiaz, P. Giorgini

21. 21 R. Ali, F. Dalpiaz, P. Giorgini Thank you! Questions? Raian Ali – ali@disi.unitn.it Fabiano Dalpiaz – dalpiaz@disi.unitn.it Paolo Giorgini – pgiorgio@disi.unitn.it

22. 22 R. Ali, F. Dalpiaz, P. Giorgini References (1) • [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

23. 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. 23 R. Ali, F. Dalpiaz, P. Giorgini

24. Location-based Tropos: metamodel Tropos Loc-based Tropos