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

2. 22 28/10/2015 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 2

3. 33 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Research question 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 analyze location-based SW?

4. 44 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini 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.

5. 55 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini 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 Feature models [Kang et al., 1998]

6. 6 1.Location modeling constructs ▫What is the conceptual framework? 2.Location relevancy ▫What should be modeled? 3.Location rules ▫Constraints of the specific location 4.Location-based behavior ▫Different behaviors are enabled/disabled depending on the current location Location-based SW: modeling challenges

7. 7 5.Hierarchical behaviors construction ▫Avoid “one location, one behavior” cases 6.Location-based behavior evaluation ▫Payoff functions to evaluate alternatives ▫Choice can be location-dependent Location-based SW: modeling challenges

8. 8 Location-based SW: features to support 1.Location identification ▫Instantiate a location model 2.Location-based behavior adaptation ▫Select the best possible behavior to achieve the goals 3.Location-based information processing ▫Information request ▫Relevant information extraction ▫Information delivery

9. 9 Location-based SW: features to support 4.Act on behalf of users ▫Location-based SW represents the user when interacting with other location actors 5.Personalization ▫Each user has a profile and preferences

10. 10 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Tropos for location-based SW: goal models

11. 11 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini 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)

12. 12 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini 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 Our solution: Location- based Tropos

13. 13 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Location-based Tropos Location-based (LB) goal models contain variation points annotated with location properties: 1.LB Or-Decomposition: the basic variability construct to express alternative goal decompositions 2.LB contribution: contributions to softgoals is location-based 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,... L3: good expertise in using PDAs and PDA has touch screen L4: low expertise in using PDA, No PDA touch screen.

14. 14 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Location-based Tropos 3.LB dependency: the actor may depend on other actors in certain locations. 4.LB Goal-Activation: location triggers goals. L5: the web-site enables payment with the customer credit card’s type L6: the assistant is idle, has a language in common with the requesting passenger,...

15. 15 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Location-based Tropos 5.LB And-Decomposition: not all and-decomposition sub- goals are needed in some location. L7: the passenger is not familiar with terminals

16. 16 Location-based Tropos process 1.Model the social structure of a location class ▫Actors and dependencies 2.Identify mobile actors ▫Those actors that need location-based SW 3.Assign a system-to-be actor to each mobile actor ▫Use goal analysis to define the rationale 4.Identify the variation points ▫Assign location properties to variation points 5.Derive a location model from location properties

17. 17 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini Location-based Tropos Location-based goal model Location model

18. 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. 19 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini 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.

20. 20 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini 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

21. 21 Thank you! Questions? Raian Ali – ali@disi.unitn.it Fabiano Dalpiaz – dalpiaz@disi.unitn.it Paolo Giorgini – pgiorgio@disi.unitn.it 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini 21

22. 22 28/10/2015 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. 23 28/10/2015 R. Ali, F. Dalpiaz, P. Giorgini 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.

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