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Model-Driven Engineering of Self-Adaptive Software UCT CS Colloquium University of Cape Town, South Africa, 19th August 2015 Thomas Vogel @tomvog System Analysis and Modeling Group Hasso Plattner Institute University of Potsdam, Germany

SLIDE 1

Model-Driven Engineering of Self-Adaptive Software

UCT CS Colloquium University of Cape Town, South Africa, 19th August 2015 Thomas Vogel @tomvog System Analysis and Modeling Group Hasso Plattner Institute University of Potsdam, Germany

SLIDE 2

Continuous Change

Software aging [Parnas, 1994]

When not being adapted to changing user needs (lack of movement) Adapting the software often violates the design (ignorant surgery)

Lehman’s laws of software evolution (real-world applications)

[Lehman and Belady, 1985, Lehman and Ramil, 2001]

I. A “system must be continually adapted else it becomes progressively

less satisfactory in use”

VI. “The functional capability of [...] systems must be continually increased

to maintain user satisfaction over the system lifetime”

⇒ Software Evolution and Maintenance

[Mens and Demeyer, 2008, Mens et al., 2010, Mens et al., 2014]

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 2

SLIDE 3

Software Evolution Process [Sommerville, 2010]

Change requests Impact analysis Release planning Fault repair Platform adaptation System enhancement Change implementation Proposed changes Requirements analysis Requirements updating Software development System release

Performed by different groups of people (support staff, developers,...)

[Kitchenham et al., 1999]

Follows a higher-level management process [Kitchenham et al., 1999] Enacting a release during scheduled system downtimes (stop-and-go maintenance) [Pezzè, 2012] ⇒ Process is costly, introduces delays, and affects availability

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 3

SLIDE 4

Software systems that are...

context-aware (pervasive computing [Weiser, 1991, Satyanarayanan, 2001],

internet of things [Perera et al., 2014])

timely changes individual changes

mission-critical/dependable [Shaw, 2002]

high or permanent availability

complex (ultra-large-scale [Northrop et al., 2006],

system of systems [Valerdi et al., 2008])

costs dynamic integration shutdown not feasible

... ⇒ Efforts and feasibility of traditional software evolution process? ⇒ Built-in evolution/adaptation process?

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 4 [acatech, 2011, p.24] distributed traffic management

SLIDE 5

Self-Adaptive Software [Cheng et al., 2009, de Lemos et al., 2013]

“systems that are able to modify their behavior and/or structure in response to their perception of the environment and the system itself, and their goals” [de Lemos et al., 2013, p. 1] Observations: Self-*: configuring / optimizing / healing / protecting / managing / ... Shift responsibility for adaptation from developers to the system Shift software engineering activities from dev. time to runtime Blurring boundary between development time and runtime Goal: Automated and dynamic adaptation Mitigating the growing costs, complexity, and diversity of adaptation

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 5

SLIDE 6

Feedback Loop [Kephart and Chess, 2003, Brun et al., 2009]

Monitor Knowledge Analyze Plan Execute Adaptable Software

Sensors Effectors

Adaptation Engine

Often inspired by control theory [Filieri et al., 2015] Turns an open-loop into a closed-loop system [Salehie and Tahvildari, 2009] Architectural blueprint: separating domain and adaptation concerns

Similar to computational reflection [Maes, 1987]

Knowledge: policies and a representation (reflection) of the adaptable software [Huebscher and McCann, 2008]

e.g., event-condition-action rules and an architectural representation

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 6

M A P E

SLIDE 7

Engineering Self-Adaptive Software

State of the Art Aims for reducing development efforts Typically, frameworks for feedback loops

Customization such as injecting policies and a representation Partial generation of feedback loops based on policies

Some Drawbacks No explicit specification and design of the feedback loops Closed approaches

Prescribe the structure and number of feedback loops Restrict the techniques/types of knowledge (policies, representation,...)

Gap between the development and runtime environments

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 7

SLIDE 8

Engineering Self-Adaptive Software with EUREMA

Side note: Model-Driven Engineering (MDE) “The term Model-Driven Engineering (MDE) is typically used to describe software development approaches in which abstract models of software systems are created and systematically transformed to concrete implementations.”

[France and Rumpe, 2007]

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 8

SLIDE 9

Engineering Self-Adaptive Software with EUREMA

Side note: Model-Driven Engineering (MDE) Goals [France and Rumpe, 2007] Mitigating the gap between the problem and solution space

Avoiding accidental complexity of closing the gap manually

Raise the level of abstraction (domain-specific languages & models) Automating development: transformation and generation Early analysis and quality assurance Promises “Industrializing” software development [Greenfield and Short, 2003] Improve developers’ productivity and software quality Reduce costs and time to market

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 9

SLIDE 10

Engineering Self-Adaptive Software with EUREMA

Side note: Model-Driven Engineering (MDE) “In our broad vision of MDE, models [...] are also the primary means by which developers and other systems understand, interact with, configure and modify the runtime behavior of software.”

[France and Rumpe, 2007]

Goals of “runtime models” Abstractions of runtime phenomena Automate runtime adaptation Analyze running software systems

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 10

SLIDE 11

EUREMA (Executable Runtime Megamodels)

Domain-specific modeling language Uses feedback loop concepts

MAPE activities, runtime models, ...

Explicit design of feedback loops Allows freely modeling feedback loops

Structure and number of loops Techniques and types of models

Runtime Interpreter EUREMA models are kept alive at runtime Directly executed by the interpreter No generation/translation steps

No gap between dev. and runtime env.

Flexibility to adapt feedback loops

Monitor Knowledge Analyze Plan Execute Adaptable Software

Sensors Effectors

Adaptation Engine Monitor Reflection Models Analyze Plan Execute Evaluation Models Change Models Monitoring Models Execution Models Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 11

SLIDE 12

Language Overview

Graphical modeling language Two kinds of diagrams

up- dated model

Update

<<Monitor>> failures

Check for failures

<<Analyze>> no failures

Architectural Model

<<ReflectionModel>>

TGG Rules

<<CausalConnectionModel>> r w r a Analyzed

Failure analysis rules

<<EvaluationModel>> r [C_SINCE(no failures) > 5]

Deep check for failures

<<Analyze>> detailed results r a

Deep analysis rules

<<EvaluationModel>> r

Repair

<<Plan>> repaired

Repair strategies

<<ChangeModel>> r w r

Effect

<<Execute>> done Executed r r [ELSE] Monitor r

Self-repair

Feedback Loop Diagram (FLD)

Layer-0 Layer-1

:Self-repair

MAPE

:mRUBiS

RtException; 10s; Monitor; r w

FLD: activities + control flow, runtime models + their usage (behavior)

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 12

SLIDE 13

Language Overview

Graphical modeling language Two kinds of diagrams

up- dated model

Update

<<Monitor>> failures

Check for failures

<<Analyze>> no failures

Architectural Model

<<ReflectionModel>>

TGG Rules

<<CausalConnectionModel>> r w r a Analyzed

Failure analysis rules

<<EvaluationModel>> r [C_SINCE(no failures) > 5]

Deep check for failures

<<Analyze>> detailed results r a

Deep analysis rules

<<EvaluationModel>> r

Repair

<<Plan>> repaired

Repair strategies

<<ChangeModel>> r w r

Effect

<<Execute>> done Executed r r [ELSE] Monitor r

Self-repair

Feedback Loop Diagram (FLD)

Layer-0 Layer-1

:Self-repair

MAPE

:mRUBiS

RtException; 10s; Monitor; r w

Layer Diagram (LD)

FLD: activities + control flow, runtime models + their usage (behavior) LD: layers, white/black-box modules + their relationships (structure) Trigger of modules: <events>;<period>;<initialState>;

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 12

i n s t a n t i a t i

SLIDE 14

Language Overview

Graphical modeling language Two kinds of diagrams

up- dated model

Update

<<Monitor>> failures

Check for failures

<<Analyze>> no failures

Architectural Model

<<ReflectionModel>>

TGG Rules

<<CausalConnectionModel>> r w r a Analyzed

Failure analysis rules

<<EvaluationModel>> r [C_SINCE(no failures) > 5]

Deep check for failures

<<Analyze>> detailed results r a

Deep analysis rules

<<EvaluationModel>> r

Repair

<<Plan>> repaired

Repair strategies

<<ChangeModel>> r w r

Effect

<<Execute>> done Executed r r [ELSE] Monitor r

Self-repair

Feedback Loop Diagram (FLD)

Layer-0 Layer-1

:Self-repair

MAPE

:mRUBiS

RtException; 10s; Monitor; r w

Layer Diagram (LD)

FLD: activities + control flow, runtime models + their usage (behavior) LD: layers, white/black-box modules + their relationships (structure) Trigger of modules: <events>;<period>;<initialState>; FLDs and LD are kept alive at runtime and executed by an interpreter

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 12

i n s t a n t i a t i

SLIDE 15

Modularity

Multiple FLDs for one feedback loop Complex model operation to invoke an FLD (entries and exists) Binding in the LD

failures

Check for failures

<<Analyze>> no failures

Architectural Model

<<ReflectionModel>> r a OK

Failure analysis rules

<<EvaluationModel>> r [C_SINCE(no failures) > 5]

Deep check for failures

<<Analyze>> detailed results r a

Deep analysis rules

<<EvaluationModel>> r Failures [ELSE] Start

Self-repair-A

up- dated model

Update

<<Monitor>>

Architectural Model

<<ReflectionModel>>

TGG Rules

w Analyzed

Analyze

Repair

<<Plan>> repaired

Repair strategies

<<ChangeModel>> r w r

Effect

<<Execute>> done Executed r r Monitor Failures r a r r

Self-repair

Start <<Analyze>>

Complex model operation

<<CausalConnectionModel>>

Layer-0 Layer-1

:Self-repair

M..PE

:mRUBiS

RtException; 10s; Monitor;

:Self-repair-A

A Analyze r w

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 13

SLIDE 16

Modularity

Multiple FLDs for one feedback loop Complex model operation to invoke an FLD (entries and exists) Binding in the LD

failures

Check for failures

<<Analyze>> no failures

Architectural Model

<<ReflectionModel>> r a OK

Failure analysis rules

<<EvaluationModel>> r [C_SINCE(no failures) > 5]

Deep check for failures

<<Analyze>> detailed results r a

Deep analysis rules

<<EvaluationModel>> r Failures [ELSE] Start

Self-repair-A

up- dated model

Update

<<Monitor>>

Architectural Model

<<ReflectionModel>>

TGG Rules

w Analyzed

Analyze

Repair

<<Plan>> repaired

Repair strategies

<<ChangeModel>> r w r

Effect

<<Execute>> done Executed r r Monitor Failures r a r r

Self-repair

Start <<Analyze>>

Complex model operation

<<CausalConnectionModel>>

Layer-0 Layer-1

:Self-repair

M..PE

:mRUBiS

RtException; 10s; Monitor;

:Self-repair-A

A Analyze r w

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 13

Binding

SLIDE 17

Variability

Alternative modules as variants Rebinding to switch between alternatives Design-time and runtime Example: different analysis techniques

Layer-0 Layer-1

:Self-repair

M..PE

:mRUBiS

RtException; 10s; Monitor;

:Self-repair-A

A Analyze

:Self-repair-A2

A r w

The same applies to implementations (black-box modules) of basic model

perations

Example: different monitoring techniques

Layer-0 Layer-1

:Self-repair

M..PE

:mRUBiS

RtException; 10s; Monitor; :selfRepair.MonitorImpl Update :selfRepair. LightWeightMonitorImpl r w

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 14

SLIDE 18

Multiple Feedback Loops

Multiple concerns to be managed Competing concerns and interferences ⇒ coordination

up- dated model

Update

<<Monitor>> bottleneck

Bottleneck identification

<<Analyze>> no bottle- necks

Architectural Model

<<ReflectionModel>> r w r Analyzed

Queueing Model

<<EvaluationModel>> r

Adjust params

<<Plan>> adjusted

Parameter variability

<<ChangeModel>> r w r

Effect

<<Execute>> done Executed r r Monitor w Analyze r w r

Self-optimization

TGG Rules

<<CausalConnectionModel>> up- dated model

Update

<<Monitor>>

Architectural Model

<<ReflectionModel>>

TGG Rules

w Analyzed

Analyze

Repair

<<Plan>> repaired

Repair strategies

<<ChangeModel>> r w r

Effect

<<Execute>> done Executed r r Monitor Failures r a r r

Self-repair

Start <<Analyze>> <<CausalConnectionModel>>

EUREMA Modeling the synchronized execution of feedback loops Model operation implementation realizes the coordination mechanism (e.g., utility functions or voting)

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 15

SLIDE 19

Multiple Feedback Loops II

Independent execution

Layer-0 Layer-1

:Self-repair

M..PE

:mRUBiS

RtException; 10s; Monitor;

:Self-repair-A

A Analyze

:Self-optimization

MAPE LoadIncrease 60s; Monitor; r r w w

Individual trigger for each feedback loop Potentially, concurrent execution of different feedback loops Possibility to implicitly synchronize the execution by triggers (e.g., appropriate frequencies of execution runs)

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 16

SLIDE 20

Multiple Feedback Loops III

Sequencing Complete Feedback Loops

Analyzed

Repair

Self- managed Self- manage Executed

Architectural Model

<<ReflectionModel>> w r w r

TGG Rules

<<CausalConnection- Model>> r r Optimize Analyzed Executed Monitor Analyze Monitor

Self-management-1

Layer-0 Layer-1

:Self-repair

M..PE

:Adaptable Software

RtException, LoadIncrease; 35s; Self-manage;

:Self-repair-A

A Analyze

:Self-optimization

MAPE

:Self-management-1

Repair Optimize r w

Explicitly modeling the synchronized execution MAPE for self-repair → MAPE for self-optimization

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 17

SLIDE 21

Multiple Feedback Loops IV

Sequencing Adaptation Activities of Feedback Loops

up- dated model

Update

<<Monitor>>

Architectural Model

<<ReflectionModel>>

TGG Rules

<<CausalConnectionModel>> r w

Effect

<<Execute>> done Self- managed r r Self-manage RepairAP Planned OptimizeAP Analyzed Planned Analyzed [ELSE] [C_SINCE( RepairAP::Planned) = 0] r w r w

Self-management-2

r <<Analyze>> <<Analyze>> <<Plan>> <<Plan>> Analyzed

Layer-0 Layer-1

:Self-repair-AP

:mRUBiS

RtException, LoadIncrease; 35s; Self-manage;

:Self-optimization-AP

AP RepairAP OptimizeAP

:Self-management-2

M..E r w

More fine-grained synchronization (activities vs. whole feedback loop) Interleaved execution of different feedback loops M → A+P for self-repair → A+P for self-optimization → E

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 18

A+P for self-optimization A+P for self-repair

SLIDE 22

Evaluation

mRUBiS as a playground Two cases

Self-healing Self-optimization

Compare alternative solutions

Models vs. code State- vs. event-based loops

with respect to

Development costs Runtime efficiency

Applied EUREMA to other approaches

Rainbow, DiVA, PLASMA

mRUBiS Architecture Item Management Service ItemRegistration Service BrowseCategories Service Authentication Service QueryService BasicQueryService BusinessObjects PersistenceService Persistence Service BusinessObjects PersistenceService Query Service QueryService BasicQuery Service Authentication Service Authentication Service BasicQueryService Reputation Service Reputation Service Authentication Service QueryService BasicQueryService BusinessObjects PersistenceService Last Second Sales Item Filter Item Filter Item Filter selection-rate-threshold:double computation-time-threshold:double last-seconds:int Future Sales Item Filter Item Filter selection-rate-threshold:double computation-time-threshold:double days-to-run:int Item Filter Item Filter Inventory Service Inventory Service QueryService BusinessObjects PersistenceService BidAndBuy Service BuyNow Service BidService Authentication Service QueryService BasicQueryService BusinessObjects PersistenceService Inventory Service User Management Service AboutMeService AuthenticationService BrowseRegionsService UserRegistrationService ViewUserInfoService QueryService BasicQueryService BusinessObjects PersistenceService privacy-level:String = "LOW"/"HIGH"

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 19

SLIDE 23

Conclusion

Summary and contributions of EUREMA

1 Integrated MDE approach 2 Open approach 3 Seamless Integration of Development and Runtime Environment 4 Adaptation and Evolution of Feedback Loops 5 State- and Event-Based Feedback Loops

Future Work Distributed feedback loops and decentralized adaptation Concurrent execution of interdependent feedback loops Model-based techniques to analyze and test EUREMA models

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 20

SLIDE 24

References I

[acatech, 2011] acatech (2011). Cyber-physical systems: Driving force for innovation in mobility, health, energy and production. acatech (National Academy of Science and Engineering) Position Paper, http://www.acatech.de/de/publikationen/stellungnahmen/acatech/detail/artikel/cyber-physical-systems.html. [Brun et al., 2009] Brun, Y., Serugendo, G. D. M., Gacek, C., Giese, H., Kienle, H., Litoiu, M., Müller, H., Pezzè, M., and Shaw, M. (2009). Engineering Self-Adaptive Systems through Feedback Loops. In Cheng, B. H., de Lemos, R., Giese, H., Inverardi, P ., and Magee, J., editors, Software Engineering for Self-Adaptive Systems, volume 5525 of Lecture Notes in Computer Science (LNCS), pages 48–70. Springer. [Cheng et al., 2009] Cheng, B. H., de Lemos, R., Giese, H., Inverardi, P ., Magee, J., Andersson, J., Becker, B., Bencomo, N., Brun, Y., Cukic, B., Serugendo, G. D. M., Dustdar, S., Finkelstein, A., Gacek, C., Geihs, K., Grassi, V., Karsai, G., Kienle, H. M., Kramer, J., Litoiu, M., Malek, S., Mirandola, R., Müller, H., Park, S., Shaw, M., Tichy, M., Tivoli, M., Weyns, D., and Whittle, J. (2009). Software Engineering for Self-Adaptive Systems: A Research Roadmap. In Cheng, B. H., de Lemos, R., Giese, H., Inverardi, P ., and Magee, J., editors, Software Engineering for Self-Adaptive Systems, volume 5525 of Lecture Notes in Computer Science (LNCS), pages 1–26. Springer. [de Lemos et al., 2013] de Lemos, R., Giese, H., Müller, H., Shaw, M., Andersson, J., Litoiu, M., Schmerl, B., Tamura, G., Villegas, N. M., Vogel, T., Weyns, D., Baresi, L., Becker, B., Bencomo, N., Brun, Y., Cukic, B., Desmarais, R., Dustdar, S., Engels, G., Geihs, K., Goeschka, K., Gorla, A., Grassi, V., Inverardi, P ., Karsai, G., Kramer, J., Lopes, A., Magee, J., Malek, S., Mankovskii, S., Mirandola, R., Mylopoulos, J., Nierstrasz, O., Pezzè, M., Prehofer, C., Schäfer, W., Schlichting, R., Smith, D. B., Sousa, J. P ., Tahvildari, L., Wong, K., and Wuttke, J. (2013). Software Engineering for Self-Adaptive Systems: A second Research Roadmap. In de Lemos, R., Giese, H., Müller, H., and Shaw, M., editors, Software Engineering for Self-Adaptive Systems II, volume 7475 of Lecture Notes in Computer Science (LNCS), pages 1–32. Springer. [Filieri et al., 2015] Filieri, A., Maggio, M., Angelopoulos, K., D?Ippolito, N., Gerostathopoulos, I., Hempel, A. B., Hoffmann, H., Jamshidi, P ., Kalyvianaki, E., Klein, C., Krikava, F ., Misailovic, S., Papadopoulos, A. V., Ray, S., Sharifloo, A. M., Shevtsov, S., Ujma, M., and Vogel, T. (2015). Software Engineering meets Control Theory. In Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS’15, page tbd. IEEE. to appear. [France and Rumpe, 2007] France, R. and Rumpe, B. (2007). Model-driven development of complex software: A research roadmap. In 2007 Future of Software Engineering, FOSE ’07, pages 37–54. IEEE. [Garlan et al., 2004] Garlan, D., Cheng, S.-W., Huang, A.-C., Schmerl, B., and Steenkiste, P . (2004). Rainbow: Architecture-Based Self-Adaptation with Reusable Infrastructure. Computer, 37(10):46–54. [Greenfield and Short, 2003] Greenfield, J. and Short, K. (2003). Software factories: Assembling applications with patterns, models, frameworks and tools. In Companion of the 18th Annual ACM SIGPLAN Conference on Object-oriented Programming, Systems, Languages, and Applications, OOPSLA ’03, pages 16–27. ACM. [Huebscher and McCann, 2008] Huebscher, M. C. and McCann, J. A. (2008). A survey of autonomic computing—degrees, models, and applications. ACM Comput. Surv., 40(3):7:1–7:28.

Thomas Vogel | Model-Driven Engineering of Self-Adaptive Software | UCT CS Colloquium | 19th Aug 2015 21

SLIDE 25

References II

[Kephart and Chess, 2003] Kephart, J. O. and Chess, D. (2003). The Vision of Autonomic Computing. Computer, 36(1):41–50. [Kitchenham et al., 1999] Kitchenham, B. A., Travassos, G. H., von Mayrhauser, A., Niessink, F ., Schneidewind, N. F ., Singer, J., Takada, S., Vehvilainen, R., and Yang, H. (1999). Towards an ontology of software maintenance. Journal of Software Maintenance: Research and Practice, 11(6):365–389. [Lehman and Belady, 1985] Lehman, M. M. and Belady, L. A., editors (1985). Program evolution: processes of software change. Academic Press Professional, Inc., San Diego, CA, USA. [Lehman and Ramil, 2001] Lehman, M. M. and Ramil, J. F . (2001). Rules and tools for software evolution planning and management.

Ann. Softw. Eng., 11(1):15–44.

[Maes, 1987] Maes, P . (1987). Concepts and experiments in computational reflection. In Conference Proceedings on Object-oriented Programming Systems, Languages and Applications, OOPSLA ’87, pages 147–155. ACM. [Mens and Demeyer, 2008] Mens, T. and Demeyer, S., editors (2008). Software Evolution. Springer. [Mens et al., 2010] Mens, T., Gueheneuc, Y.-G., Fernandez-Ramil, J., and D’Hondt, M. (2010). Guest editors’ introduction: Software evolution. IEEE Software, 27(4):22–25. [Mens et al., 2014] Mens, T., Serebrenik, A., and Cleve, A., editors (2014). Evolving Software Systems. Springer. [Morin et al., 2009] Morin, B., Barais, O., Jézéquel, J.-M., Fleurey, F ., and Solberg, A. (2009). Models@ Run.time to Support Dynamic Adaptation. Computer, 42(10):44–51. [Northrop et al., 2006] Northrop, L., Feiler, P ., Gabriel, R. P ., Goodenough, J., Linger, R., Longstaff, T., Kazman, R., Klein, M., Schmidt, D., Sullivan, K., and Wallnau, K. (2006). Ultra-Large-Scale Systems: The Software Challenge of the Future. Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA. [Parnas, 1994] Parnas, D. L. (1994). Software aging. In Proceedings of the 16th International Conference on Software Engineering, ICSE ’94, pages 279–287. IEEE. [Perera et al., 2014] Perera, C., Zaslavsky, A., Christen, P ., and Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. IEEE Communications Surveys & Tutorials, 16(1):414–454.

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References III

[Pezzè, 2012] Pezzè, M. (2012). From off-Line to continuous on-line maintenance. In 28th IEEE International Conference on Software Maintenance, ICSM ’12, pages 2–3. IEEE. [Salehie and Tahvildari, 2009] Salehie, M. and Tahvildari, L. (2009). Self-adaptive software: Landscape and research challenges. ACM Trans. Auton. Adapt. Syst., 4(2):1–42. [Satyanarayanan, 2001] Satyanarayanan, M. (2001). Pervasive Computing: Vision and Challenges. IEEE Personal Communications, 8(4):10–17. [Shaw, 2002] Shaw, M. (2002). Everyday dependability for everyday needs. In Supplemental Proceedings of the 13th International Symposium on Software Reliability Engineering, ISSRE ’02, pages 7–11. IEEE. (keynote). [Sommerville, 2010] Sommerville, I. (2010). Software Engineering. Addison-Wesley, 9 edition. [Tajalli et al., 2010] Tajalli, H., Garcia, J., Edwards, G., and Medvidovic, N. (2010). Plasma: A plan-based layered architecture for software model-driven adaptation. In Proceedings of the IEEE/ACM International Conference on Automated Software Engineering, ASE ’10, pages 467–476. ACM. [Valerdi et al., 2008] Valerdi, R., Axelband, E., Baehren, T., Boehm, B., Dorenbos, D., Jackson, S., Madni, A., Nadler, G., Robitaille, P ., and Settles, S. (2008). A research agenda for systems of systems architecting. International Journal of System of Systems Engineering, 1(1–2):171–188. [Vogel and Giese, 2012] Vogel, T. and Giese, H. (2012). A Language for Feedback Loops in Self-Adaptive Systems: Executable Runtime Megamodels. In Proceedings of the 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2012), pages 129–138. IEEE. [Vogel and Giese, 2014] Vogel, T. and Giese, H. (2014). Model-Driven Engineering of Self-Adaptive Software with EUREMA. ACM Trans. Auton. Adapt. Syst., 8(4):18:1–18:33. [Weiser, 1991] Weiser, M. (1991). The Computer for the 21st Century. Scientific American, 265(3):94–104.

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