Runtime Monitoring of Functional Component Changes with Behavior Models

We consider the problem of run-time discovery and continuous monitoring of new components that live in an open environment. We focus on extracting a formal model—which may not be available— by observing the behavior of the running component. We show how the model built at run time can be enriched through new observations (dy- namic model update). We also use the inferred model to perform run- time verification. That is, we try to identify if any changes are made to the component that modify its original behavior, contradict the previous observations, and invalidate the inferred model

Detecting component changes at run time with behavior models

Modern software systems are composed of several services which may be developed and maintained by third parties and thus they can change independently and without notice during the system’s runtime execution. In such systems, changes may possibly be a threat to system functional correctness, and thus to its reliability. Hence, it is important to detect them as soon as they happen to enable proper reaction. Change detection can be done by monitoring system execution and comparing the observed execution traces against models of the services composing the application. Unfortunately, formal specifications for services are not usually provided and developers have to infer them. In this paper we propose a methodology which exactly addresses these issues by using software behavior models to monitor component execution and detect changes. In particular, we describe a technique to infer behavior model specifications with a dynamic black box approach, keep them up-to-date with run time observations and detect behavior changes. Finally, we present a case study to validate the effectiveness of the approach in component change detection for a component that implements a complex, real communication protocol.European Commission (Programme IDEAS-ERC, Project 227977-SMScom

Self-adaptive unobtrusive interactions of mobile computing systems

[EN] In Pervasive Computing environments, people are surrounded by a lot of embedded services. Since pervasive devices, such as mobile devices, have become a key part of our everyday life, they enable users to always be connected to the environment, making demands on one of the most valuable resources of users: human attention. A challenge of the mobile computing systems is regulating the request for users¿ attention. In other words, service interactions should behave in a considerate manner by taking into account the degree to which each service intrudes on the user¿s mind (i.e., the degree of obtrusiveness). The main goal of this paper is to introduce self-adaptive capabilities in mobile computing systems in order to provide non-disturbing interactions. We achieve this by means of an software infrastructure that automatically adapts the service interaction obtrusiveness according to the user¿s context. This infrastructure works from a set of high-level models that define the unobtrusive adaptation behavior and its implication with the interaction resources in a technology-independent way. Our infrastructure has been validated through several experiments to assess its correctness, performance, and the achieved user experience through a user study.This work has been developed with the support of MINECO under the project SMART-ADAPT TIN2013-42981-P, and co-financed by the Generalitat Valenciana under the postdoctoral fellowship APOSTD/2016/042.Gil Pascual, M.; Pelechano Ferragud, V. (2017). Self-adaptive unobtrusive interactions of mobile computing systems. Journal of Ambient Intelligence and Smart Environments. 9(6):659-688. https://doi.org/10.3233/AIS-170463S65968896Aleksy, M., Butter, T., & Schader, M. (2008). Context-Aware Loading for Mobile Applications. Lecture Notes in Computer Science, 12-20. doi:10.1007/978-3-540-85693-1_3Y. Bachvarova, B. van Dijk and A. Nijholt, Towards a unified knowledge-based approach to modality choice, in: Proc. Workshop on Multimodal Output Generation (MOG), 2007, pp. 5–15.Barkhuus, L., & Dey, A. (2003). Is Context-Aware Computing Taking Control away from the User? Three Levels of Interactivity Examined. Lecture Notes in Computer Science, 149-156. doi:10.1007/978-3-540-39653-6_12Bellotti, V., & Edwards, K. (2001). Intelligibility and Accountability: Human Considerations in Context-Aware Systems. Human–Computer Interaction, 16(2-4), 193-212. doi:10.1207/s15327051hci16234_05D. Benavides, P. Trinidad and A. Ruiz-Cortés, Automated reasoning on feature models, in: Proceedings of the 17th International Conference on Advanced Information Systems Engineering, CAiSE’05, Springer-Verlag, Berlin, 2005, pp. 491–503.Bernsen, N. O. (1994). Foundations of multimodal representations: a taxonomy of representational modalities. Interacting with Computers, 6(4), 347-371. doi:10.1016/0953-5438(94)90008-6Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., & Riboni, D. (2010). A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing, 6(2), 161-180. doi:10.1016/j.pmcj.2009.06.002Blumendorf, M., Lehmann, G., & Albayrak, S. (2010). Bridging models and systems at runtime to build adaptive user interfaces. Proceedings of the 2nd ACM SIGCHI symposium on Engineering interactive computing systems - EICS ’10. doi:10.1145/1822018.1822022D.M. Brown, Communicating Design: Developing Web Site Documentation for Design and Planning, 2nd edn, New Riders Press, 2010.J. Bruin, Statistical Analyses Using SPSS, 2011, http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm#1sampt.J. Cámara, G. Moreno and D. Garlan, Reasoning about human participation in self-adaptive systems, in: SEAMS 2015, 2015, pp. 146–156.Campbell, A., & Choudhury, T. (2012). From Smart to Cognitive Phones. IEEE Pervasive Computing, 11(3), 7-11. doi:10.1109/mprv.2012.41Y. Cao, M. Theune and A. Nijholt, Modality effects on cognitive load and performance in high-load information presentation, in: Proceedings of the 14th International Conference on Intelligent User Interfaces, IUI’09, ACM, New York, 2009, pp. 335–344.Chang, F., & Ren, J. (2007). Validating system properties exhibited in execution traces. Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering - ASE ’07. doi:10.1145/1321631.1321723H. Chen and J.P. Black, A quantitative approach to non-intrusive computing, in: Mobiquitous’08: Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST, Brussels, 2008, pp. 1–10.Chittaro, L. (2010). Distinctive aspects of mobile interaction and their implications for the design of multimodal interfaces. Journal on Multimodal User Interfaces, 3(3), 157-165. doi:10.1007/s12193-010-0036-2Clerckx, T., Vandervelpen, C., & Coninx, K. (2008). Task-Based Design and Runtime Support for Multimodal User Interface Distribution. Lecture Notes in Computer Science, 89-105. doi:10.1007/978-3-540-92698-6_6Cook, D. J., & Das, S. K. (2012). Pervasive computing at scale: Transforming the state of the art. Pervasive and Mobile Computing, 8(1), 22-35. doi:10.1016/j.pmcj.2011.10.004Cornelissen, B., Zaidman, A., van Deursen, A., Moonen, L., & Koschke, R. (2009). A Systematic Survey of Program Comprehension through Dynamic Analysis. IEEE Transactions on Software Engineering, 35(5), 684-702. doi:10.1109/tse.2009.28Czarnecki, K. (2004). Generative Software Development. Lecture Notes in Computer Science, 321-321. doi:10.1007/978-3-540-28630-1_33M. de Sá, C. Duarte, L. Carriço and T. Reis, Designing mobile multimodal applications, in: Information Science Reference, 2010, pp. 106–136, Chapter 5.C. Duarte and L. Carriço, A conceptual framework for developing adaptive multimodal applications, in: Proceedings of the 11th International Conference on Intelligent User Interfaces, IUI’06, ACM, New York, 2006, pp. 132–139.Evers, C., Kniewel, R., Geihs, K., & Schmidt, L. (2014). The user in the loop: Enabling user participation for self-adaptive applications. Future Generation Computer Systems, 34, 110-123. doi:10.1016/j.future.2013.12.010Fagin, R., Halpern, J. Y., & Megiddo, N. (1990). A logic for reasoning about probabilities. Information and Computation, 87(1-2), 78-128. doi:10.1016/0890-5401(90)90060-uFerscha, A. (2012). 20 Years Past Weiser: What’s Next? IEEE Pervasive Computing, 11(1), 52-61. doi:10.1109/mprv.2011.78Floch, J., Frà, C., Fricke, R., Geihs, K., Wagner, M., Lorenzo, J., … Scholz, U. (2012). Playing MUSIC - building context-aware and self-adaptive mobile applications. Software: Practice and Experience, 43(3), 359-388. doi:10.1002/spe.2116Gibbs, W. W. (2005). Considerate Computing. Scientific American, 292(1), 54-61. doi:10.1038/scientificamerican0105-54Gil, M., Giner, P., & Pelechano, V. (2011). Personalization for unobtrusive service interaction. Personal and Ubiquitous Computing, 16(5), 543-561. doi:10.1007/s00779-011-0414-0Gil Pascual, M. (s. f.). Adapting Interaction Obtrusiveness: Making Ubiquitous Interactions Less Obnoxious. A Model Driven Engineering approach. doi:10.4995/thesis/10251/31660Haapalainen, E., Kim, S., Forlizzi, J. F., & Dey, A. K. (2010). Psycho-physiological measures for assessing cognitive load. Proceedings of the 12th ACM international conference on Ubiquitous computing - Ubicomp ’10. doi:10.1145/1864349.1864395Hallsteinsen, S., Geihs, K., Paspallis, N., Eliassen, F., Horn, G., Lorenzo, J., … Papadopoulos, G. A. (2012). A development framework and methodology for self-adapting applications in ubiquitous computing environments. Journal of Systems and Software, 85(12), 2840-2859. doi:10.1016/j.jss.2012.07.052Hassenzahl, M. (2004). The Interplay of Beauty, Goodness, and Usability in Interactive Products. Human-Computer Interaction, 19(4), 319-349. doi:10.1207/s15327051hci1904_2Hassenzahl, M., & Tractinsky, N. (2006). User experience - a research agenda. Behaviour & Information Technology, 25(2), 91-97. doi:10.1080/01449290500330331Ho, J., & Intille, S. S. (2005). Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ’05. doi:10.1145/1054972.1055100Horvitz, E., Kadie, C., Paek, T., & Hovel, D. (2003). Models of attention in computing and communication. Communications of the ACM, 46(3), 52. doi:10.1145/636772.636798Horvitz, E., Koch, P., Sarin, R., Apacible, J., & Subramani, M. (2005). Bayesphone: Precomputation of Context-Sensitive Policies for Inquiry and Action in Mobile Devices. Lecture Notes in Computer Science, 251-260. doi:10.1007/11527886_33Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41-50. doi:10.1109/mc.2003.1160055Korpipaa, P., Malm, E.-J., Rantakokko, T., Kyllonen, V., Kela, J., Mantyjarvi, J., … Kansala, I. (2006). Customizing User Interaction in Smart Phones. IEEE Pervasive Computing, 5(3), 82-90. doi:10.1109/mprv.2006.49S. Lemmelä, A. Vetek, K. Mäkelä and D. Trendafilov, Designing and evaluating multimodal interaction for mobile contexts, in: Proceedings of the 10th International Conference on Multimodal Interfaces, ICMI’08, ACM, New York, 2008, pp. 265–272.Lim, B. Y. (2010). Improving trust in context-aware applications with intelligibility. Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Ubicomp ’10. doi:10.1145/1864431.1864491J.-Y. Mao, K. Vredenburg, P.W. Smith and T. Carey, User-centered design methods in practice: A survey of the state of the art, in: Proceedings of the 2001 Conference of the Centre for Advanced Studies on Collaborative Research, CASCON’01, IBM Press, 2001, p. 12.Maoz, S. (2009). Using Model-Based Traces as Runtime Models. Computer, 42(10), 28-36. doi:10.1109/mc.2009.336Mayer, R. E., & Moreno, R. (2003). Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist, 38(1), 43-52. doi:10.1207/s15326985ep3801_6Motti, V. G., & Vanderdonckt, J. (2013). A computational framework for context-aware adaptation of user interfaces. IEEE 7th International Conference on Research Challenges in Information Science (RCIS). doi:10.1109/rcis.2013.6577709R. Murch, Autonomic Computing, IBM Press, 2004.Obrenovic, Z., Abascal, J., & Starcevic, D. (2007). Universal accessibility as a multimodal design issue. Communications of the ACM, 50(5), 83-88. doi:10.1145/1230819.1241668Patterson, D. J., Baker, C., Ding, X., Kaufman, S. J., Liu, K., & Zaldivar, A. (2008). Online everywhere. Proceedings of the 10th international conference on Ubiquitous computing - UbiComp ’08. doi:10.1145/1409635.1409645Pielot, M., de Oliveira, R., Kwak, H., & Oliver, N. (2014). Didn’t you see my message? Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI ’14. doi:10.1145/2556288.2556973Poppinga, B., Heuten, W., & Boll, S. (2014). Sensor-Based Identification of Opportune Moments for Triggering Notifications. IEEE Pervasive Computing, 13(1), 22-29. doi:10.1109/mprv.2014.15S. Ramchurn, B. Deitch, M. Thompson, D. De Roure, N. Jennings and M. Luck, Minimising intrusiveness in pervasive computing environments using multi-agent negotiation, in: Mobile and Ubiquitous Systems: Networking and Services, MOBIQUITOUS 2004. The First Annual International Conference on, 2004, pp. 364–371.C. Roda, Human Attention and Its Implications for Human-Computer Interaction, Cambridge University Press, 2011.S. Rosenthal, A.K. Dey and M. Veloso, Using decision-theoretic experience sampling to build personalized mobile phone interruption models, in: Proceedings of the 9th International Conference on Pervasive Computing, Pervasive 2011, Springer-Verlag, Berlin, 2011, pp. 170–187.E. Rukzio, K. Leichtenstern and V. Callaghan, An experimental comparison of physical mobile interaction techniques: Touching, pointing and scanning, in: 8th International Conference on Ubiquitous Computing, UbiComp 2006, Orange County, California, 2006.Serral, E., Valderas, P., & Pelechano, V. (2010). Towards the Model Driven Development of context-aware pervasive systems. Pervasive and Mobile Computing, 6(2), 254-280. doi:10.1016/j.pmcj.2009.07.006D. Siewiorek, A. Smailagic, J. Furukawa, A. Krause, N. Moraveji, K. Reiger, J. Shaffer and F.L. Wong, Sensay: A context-aware mobile phone, in: Proceedings of the 7th IEEE International Symposium on Wearable Computers, ISWC’03, IEEE Computer Society, Washington, 2003, p. 248.Tedre, M. (2006). What should be automated? Proceedings of the 1st ACM international workshop on Human-centered multimedia - HCM ’06. doi:10.1145/1178745.1178753M. Valtonen, A.-M. Vainio and J. Vanhala, Proactive and adaptive fuzzy profile control for mobile phones, in: IEEE International Conference on Pervasive Computing and Communications, 2009, PerCom, 2009, pp. 1–3.Vastenburg, M. H., Keyson, D. V., & de Ridder, H. (2007). Considerate home notification systems: a field study of acceptability of notifications in the home. Personal and Ubiquitous Computing, 12(8), 555-566. doi:10.1007/s00779-007-0176-xWarnock, D., McGee-Lennon, M., & Brewster, S. (2011). The Role of Modality in Notification Performance. Lecture Notes in Computer Science, 572-588. doi:10.1007/978-3-642-23771-3_43Weiser, M., & Brown, J. S. (1997). The Coming Age of Calm Technology. Beyond Calculation, 75-85. doi:10.1007/978-1-4612-0685-9_6Van Woensel, W., Gil, M., Casteleyn, S., Serral, E., & Pelechano, V. (2013). Adapting the Obtrusiveness of Service Interactions in Dynamically Discovered Environments. Mobile and Ubiquitous Systems: Computing, Networking, and Services, 250-262. doi:10.1007/978-3-642-40238-8_2

a bottom-up approach for model-based software development

Models in software engineering are descriptive structures so that transformations can connect their contents at a semantic level. In model-based software development, algorithmic program code usually exists alongside models - derived from them or with the purpose to amend them. While thus both kinds of notations must be considered by developers, no consistent mapping is given since transformations between models and code are usually unidirectional for code generation. This impedes a continuous integration of both, limits the applicability of models, and prevents error tracking and monitoring at run time with respect to models. In this thesis, the approach of embedded models is introduced. Embedded models define patterns in program code whose elements have formal relations to models and can be executed by reflection at the same time. Model specifications are thus embedded in implementations and can be accessed by bidirectional transformations for design, verification, execution, and monitoring. The thesis focuses on the development of such patterns and their precise description as well as on the connection to other program code surrounding embedded models. Implementations are described for two modeling domains, state machines and process models, including tools for design, verification, execution, monitoring, and design recovery. The approach is evaluated with two case studies, the modeling of a real-world load generator for performance tests and the development of model-based educational graphical scenarios for university teaching. Both case studies show that the approach is valid and fulfills its purpose for a certain class of applications. Focusing on the integration in implementations, embedded models are thus a bottom-up approach for model-based software development

Descriptive business process models at run-time

Today's competitive markets require organisations to react proactively to changes in their environment if financial and legal consequences are to be avoided. Since business processes are elementary parts of modern organisations they are also required to efficiently adapt to these changes in quick and flexible ways. This requirement demands a more dynamic handling of business processes, i.e. treating business processes as run-time artefacts rather than design-time artefacts. One general approach to address this problem is provided by the community of [email protected], which promotes methodologies concerned with self-adaptive systems where models reflect the system's current state at any point in time and allow immediate reasoning and adaptation mechanisms. However, in contrast to common self-adaptive systems the domain of business processes features two additional challenges: (i) a bigger than usual abstraction gap between the business process models and the actual run-time information of the enterprise system and (ii) the possibility of run-time deviations from the planned models. Developing an understanding of such processes is a crucial necessity in order to optimise business processes and dynamically adapt to changing demands. This thesis explores the potential of adopting and enhancing principles and mechanisms from the [email protected] domain to the business process domain for the purpose of run-time reasoning, i.e. investigating the potential role of Descriptive Business Process Models at Run-time (DBPMRTs) in the business process management domain. The DBPMRT is a model describing the enterprise system at run-time and thus enabling higher-level reasoning on the as-is state. Along with the specification of the DBPMRT, algorithms and an overall framework are proposed to establish and maintain a causal link from the enterprise system to the DBPMRT at run-time. Furthermore, it is shown that proactive higher-level reasoning on a DBPMRT in the form of performance prediction allows for more accurate results. By taking these steps the thesis addresses general challenges of business process management, e.g. dealing with frequently changing processes and shortening the business process life cycle. At the same time this thesis contributes to research in [email protected] by providing a complex real-world use case as well as a reference approach for dealing with volatile [email protected] of a higher abstraction level