1. Collaboration opportunities and wrap-up
Part 1:
Overview of S-Cube
Part 2:
Service engineering research in S-Cube and links with industrial use cases
Part 3:
Collaboration opportunities and wrap-up

2. Details on the S-Cube Integrated Research Framework
Outline
Details on the S-Cube Integrated Research Framework
Research Areas
Service Engineering
Cross-layer Adaptation
End-to-end Quality Provision
Case studies: a methodological approach
(c) Prof. Dr. Klaus Pohl

3. Details on the IRF

4. S-Cube Integrated Research Framework
S-Cube focuses on long-term research
Main research focus:
Software service and systems
Adaptivity and evolution
of services
of agile service networks
S-Cube developed an Integrated Research Framework
4 views on research issues
S-Cube evolved a methodology for case studies documentation
(c) Prof. Dr. Klaus Pohl

5. Integrated Research Framework Views
Conceptual Research Framework
Reference Life Cycle
Logical Run Time Architecture
Logical Design Environment
(c) Prof. Dr. Klaus Pohl

6. Integrated Research Framework Conceptual Research Framework
© S-Cube – 6
(c) Prof. Dr. Klaus Pohl

7. Integrated Research Framework Reference Life-Cycle
© S-Cube – 7
(c) Prof. Dr. Klaus Pohl

8. Integrated Research Framework Logical Run-Time Architecture
© S-Cube – 8
(c) Prof. Dr. Klaus Pohl

9. Integrated Research Framework Logical Design Environment
© S-Cube – 9
(c) Prof. Dr. Klaus Pohl

10. Research Areas in Service Engineering

11. Service Engineering for Service-Based Applications (SBA)
(c) Prof. Dr. Klaus Pohl

12. Service Techniques & Methods Planes
Adaptation & Monitoring
(SAM)
Business
Process
Mgt. (BPM)
Adaptation & Monitoring
(SAM)
Engineering & Design
(SED)
Composition &
Coordination
(SCC)
Infra- structure
(SI)
Service Technologies
Layers
Quality Definition, Negotiation & Assurance
(SQDNA)
© S-Cube – 3/#
(c) Prof. Dr. Klaus Pohl

13. Adaptation design Focus on activation of adaptation strategies
Instance-level adaptation
Context
Model-based
(c) Prof. Dr. Klaus Pohl

14. Main Ingredients of an Adaptable SBA [6]
Bucchiarone, C. Cappiello, E. di Nitto, R. Kazhamiakin, V. Mazza, and M. Pistore,
“Design for adaptation of Service-Based applications: Main issues and requirements,” in WEOSA 2009
(c) Prof. Dr. Klaus Pohl

15. Life-cycle for SBAs
WESOA, 2009
(c) Prof. Dr. Klaus Pohl

16. Adaptation Strategies
To mantain aligned the application behaviour with the context and system requirements
Service substitution
Re-execution
(Re-)negotiation
(Re-)composition
Compensation
Log/Update adaptation Information
Fail
WESOA, 2009
(c) Prof. Dr. Klaus Pohl

17. Adaptation Triggers
The adaptation may be motivated by a variety of triggers
Component Services
Service functionality
Service quality
SBAs context
Business context
Computational context
User context
WESOA, 2009
(c) Prof. Dr. Klaus Pohl

18. Adaptation Strategies & Triggers
To re-align the application within the system and/or context requirements
Each trigger can be associated with a set of adaptation strategies
WESOA, 2009
(c) Prof. Dr. Klaus Pohl
(c) Prof. Dr. Klaus Pohl 18

19. Design Guidelines for triggers and adaptation strategies
Design adaptable SBAs implies relate adaptation triggers and adaptation strategies together
Modeling adaptation triggers
Realizing adaptation strategies
Associating adaptation strategies to triggers
Design approaches
Built-in adaptation
Adaptation needs and adaptation configuration known a priori
Abstraction-based adaptation
Adaptation need fixed, but adaptation configuration not known a priori
Dynamic adaptation
Adaptation needs not known at design time or cannot be enumerated
WESOA, 2009
(c) Prof. Dr. Klaus Pohl

20. Designing reliable service compositions
Focus on service compositions
To design more reliable service-based processes
inserting monitors, adaptation strategies, changing process structure, …
Which is the best choice?
Context-awareness (user-dependent)
Based on quality evaluation
Cappiello, C.; Pernici, B.: QUADS: Quality-Aware Design of dependable Service-based processes. 2010
(c) Prof. Dr. Klaus Pohl

21. Cappiello, 2010
(c) Prof. Dr. Klaus Pohl

22. Preventive and Corrective strategies
Cappiello, 2010
(c) Prof. Dr. Klaus Pohl

23. Quality evaluation Representing users
According to quality evaluation techniques for service compositions
Representing users
Importance of each user
Importance of quality dimensions for each user
Cappiello, 2010
(c) Prof. Dr. Klaus Pohl

24. Comparing strategies
Domain specific!
(c) Prof. Dr. Klaus Pohl

25. Evaluating alternative strategies for a service
Example: Redundancy selected
additional quality constraints for redundant services are derived at design time
basis for service selection at run time
(c) Prof. Dr. Klaus Pohl

26. Cross-Layer Adaptation
(c) Prof. Dr. Klaus Pohl

27. Service Techniques & Methods Planes
Adaptation & Monitoring
(SAM)
Engineering & Design
(SED)
Business
Process
Mgt. (BPM)
Adaptation & Monitoring
(SAM)
Composition &
Coordination
(SCC)
Infra- structure
(SI)
Service Technologies
Layers
Quality Definition, Negotiation & Assurance
(SQDNA)
© S-Cube – 3/#
(c) Prof. Dr. Klaus Pohl

28. WP Vision Key Challenges
Comprehensive and integrated adaptation and monitoring principles, techniques, and methodologies
Across SBA layers, across SBA boundaries, across SBA life-cycle
Context- and HCI-aware A&M
Improve SBA adaptation based on the contextual knowledge
Mixed initiative SBA adaptation
From self-adaptation to human-in-the-loop adaptation
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 4/#
(c) Prof. Dr. Klaus Pohl

29. WP Vision Integrated A&M Framework
Conceptual Model:
A&M taxonomy
Instantiations:
Cross-layer A&M
Proactive adaptation
HCI and context-awareness
Self-adaptation
Monitoring mechanisms
Adaptation mechanisms
Monitored events
Adaptation requirements
Adaptation strategies
detect
trigger
achieve
realize
Conceptual architecture:
Results from SotA
Research results of S-Cube members
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 5/#
(c) Prof. Dr. Klaus Pohl

30. Key Results Achieved Cross-layer Adaptation and Monitoring
REQUIREMENTS
Cross-layer integrated monitoring mechanisms
Monitored events
Monitoring mechanisms
Means to identify adaptation needs across layers
Adaptation effectiveness
Event propagation and alignment
Cross-layer Models
application
monitoring events
adaptation strategies
Adaptation requirements
Means to identify adaptation strategies across layers
Adaptation compatibility and integrity
Adaptation coordination
Adaptation strategies
Adaptation mechanisms
Cross-layer integrated and coordinated adaptation mechanisms
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 9/#
(c) Prof. Dr. Klaus Pohl

31. Cross-layer Adaptation and Monitoring Integrated Monitoring Mechanisms
Results
Unify monitoring of business properties and low-level service properties centered around service compositions at run-time [1]
Instance and class properties in the same model (ASTRO)
Rich notation for basic events and probes (Dynamo)
Integrate run-time and design-time events monitoring [2]
Monitoring of run-time properties and model changes (EMF events, from design environment) in the same framework (WildCat model for monitored data)
Multifactor Monitoring [3]
Unified language for querying various factors: service behavior, service quality, service context, service structure
Formal model for the representation of queries: event calculus
Automated translation rules for different factors
Baresi, Guinea, Pistore, Trainotti. Dynamo+ASTRO: an Integrated Approach for BPEL Monitoring. In ICWS 2009
Morin, Ledoux, Ben Hassine, Chauvel, Barais, Jezequel. Unifying Runtime Adaptation and Design Evolution In CIT 2009
Zisman, Spanoudakis, Mahbub. A Monitoring Approach for Run-time Service Discovery. Under submission.
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
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(c) Prof. Dr. Klaus Pohl

32. Cross-layer Adaptation and Monitoring Identification of Adaptation Needs
Results
Domain assumptions [1]
Explicit encoding of domain assumptions
Associate them to requirements
Monitor assumption violations
verify requirements at run time to trigger adaptation
Replacement policies [2]
Explicit encoding of rules for service substitution
Take into account the SBA execution point
Consider variety of aspects: QoS, structural, behavior, context, changing requirements
Take into account availability of new services
Gehlert, Bucchiarone, Kazhamiakin, Metzger, Pistore, Pohl: Exploiting Assumption-Based Verification for the Adaptation of Service-Based Applications. Conference, 2010
Mahbub, Zisman. Replacement Policies for Service-Based Systems. MONA+ workshop, ICSOC/ServiceWave conference, 2009
Assumptions
Domain
Requirements
SBA
design time
run-time
Non-functional req’s
Functional req’s
Composition (BPEL)
Service protocols
QoS models
External services
User context
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 32/#
(c) Prof. Dr. Klaus Pohl

33. Cross-layer Adaptation and Monitoring Identification of Adaptation Strategies
Results
Adaptation based on quality factor analysis
Identify influential factors for KPI violations across layers: data mining techniques
Identify adaptation requirements: analysis of the dependency trees
Associate adaptation actions to basic SBA metrics and properties
Identify adaptation strategy based on the effects of adaptation actions
Kazhamiakin, Wetzstein, Karastoyanova, Pistore, Leymann. SBA Adaptation based on quality factor analysis . MONA+ Workshop, ICSOC/ServiceWave Conference 2009
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
© S-Cube – 33/#
(c) Prof. Dr. Klaus Pohl

34. Cross-layer Adaptation and Monitoring Coordinated Adaptation
Results
Framework for self-supervising processes
Generic adaptation framework with uniform and extendable language for adaptation coordination
Encoding of recovery actions
Encoding of coordinated strategies
Wide range of adaptation actions
Service-level: ignore, halt, retry, rebind
Process-level: change parameters, change partner, restore, recovery subprocess
Pluggable run-time architecture to accommodate different adaptation types
AOP techniques for extending process engine functionalities
Baresi, Guinea, Pasquale. Integrated and Composable Supervision of BPEL processes. ICSOC Conference 2008
Baresi, Guinea. Self-supervising BPEL processes. PoliMi TR
R.Kazhamiakin / WP-JRA-1.2 – Month 24 Review Meeting
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(c) Prof. Dr. Klaus Pohl

35. End-to-End Quality Provision
(c) Prof. Dr. Klaus Pohl

36. Service Techniques & Methods Planes
Adaptation & Monitoring
(SAM)
Engineering & Design
(SED)
Business
Process
Mgt. (BPM)
Adaptation & Monitoring
(SAM)
Composition &
Coordination
(SCC)
Infra- structure
(SI)
Service Technologies
Layers
Quality Definition, Negotiation & Assurance
(SQDNA)
© S-Cube – 3/#
(c) Prof. Dr. Klaus Pohl

37. Motivational „Scenario“
response
time: 1 s
time: 2 s
2. Pay
Renewal Fee
3. Update Vehicle Record
4.a. Confirmation
1. Identify Vehicle
[valid]
[not valid]
ePay
response time: 2 s
cost: 1.50 €
SecurePay
response time: 3 s
cost: 1 €
Yahoo
response time: 1.5 s
GMail
4.b. Mail Validation Sticker
= service invocation/activity
= third-party service
= internal service
= alternative binding
RenewalHandler
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
TODO: show what end-to-end means,
Negotiation and assurance

38. WP Vision Key Challenges
Devise novel principles, techniques & methods for Quality...
...Definition [Mo1-45] D
End-to-End Quality Reference Model (completed in Y1)
Rich and Extensible Quality Definition Language
Exploiting HCI knowledge for automatic quality contract establishment
Proactive SLA negotiation and agreement (from Y3)
Run-time Quality Assurance Techniques
Quality Prediction Techniques to Support Proactive Adaptation A
Mention that explicit negotiation is not always needed; e.g., in case there are taci agreements / contracts (e.g., using freely available services, …) N
...Assurance [Mo18–45]
...Negotiation [Mo10–45]
(c) Prof. Dr. Klaus Pohl

39. Key Results Achieved Quality Reference Model
Problems
Understanding quality attributes across SOA layers
Solution
Quality Reference Model
S-Cube Publications
NNN
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 39/<Mx>

40. Key Results Achieved Knowledge Modelling
S-Cube Quality Reference Model (QRM)
Model containing 77 quality attributes in 10 categories
 Deliverable CD-JRA [Mo 12]: “Quality reference model for SBAs”
SLOs accessible via
KM!!!
A. Metzger / WP-JRA-1.3 – Month 12 Review Meeting, Essen, April 2009
© S-Cube – 9/18
(c) Prof. Dr. Klaus Pohl

41. Key Results Achieved Knowledge Modelling
S-Cube Quality Reference Model (QRM)
Approach
Data collection: describe most important quality models in disciplines
Quality attributes analysis: identify relevant attributes
Consolidation: synthesize S-Cube QRM from quality attributes / models
Models Analyzed
ISO Software Quality Model
UML-Based Quality Models
Statically Inferred QoS Attributes Model
Design by Contracts Models
Functional Quality in Service Composition Model
Service Networks and KPIs Model
Grid Quality Model
Softw.
Eng.
SOC
BPM
Grid
A. Metzger / WP-JRA-1.3 – Month 12 Review Meeting, Essen, April 2009
© S-Cube – 10/18
(c) Prof. Dr. Klaus Pohl

42. Exploiting HCI knowledge for automatic quality contract establishment
Problems
Need for automated service contract negotiation (time can be critical factor)
User interaction and experience impacts on negotiation
Quality modelling languages offer limited capabilities to support automated negotiation
Lack of formalization (hinders automation)
Negotiation-related concepts missing; e.g., negotiatable vs. non-negotiatable attributes
Missing shared terminology between provider and consumers
Solution
Quality meta-model (QMM) encompassing the concepts for a rich, extensible, and semantically enriched quality definition language
Automated negotiation techniques based on QMM concepts
Considering codified UI aspects in QMM and negotiation techniques
S-Cube Publications
Marco Comuzzi and Barbara Pernici. A Framework for QoS-Based Web service Contracting. In ACM Transactions on the Web, 3(3), 2009
Marco Comuzzi, Kyriakos Kritikos and Pierluigi Plebani. Semantic-aware Service Quality Negotiation. In ServiceWave 2008
Marco Comuzzi, Kyriakos Kritikos and Pierluigi Plebani. A semantic based framework for supporting negotiation in Service Oriented Architectures. In Proceedings IEEE CEC 2009
Kyriakos Kritikos and Dimitris Plexousakis. Mixed-Integer Programming for QoS-Based Web Service Matchmaking. In IEEE Transactions on Services Computing, 2(2), 2009
negotiatable vs. non-negotiatable attributes: performance vs. Reputation
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 42/<Max>
(c) Prof. Dr. Klaus Pohl

43. © S-Cube – 43/<Max>
Automatic quality contract establishment Foundation: Quality Definition D N
S-Cube Quality Meta Model (Excerpt) [1, 2, 3, CD-JRA-1.3.3]
Based on Quality Reference Model [CD-JRA-1.3.2], built from quality attributes relevant for each of the layers ( JRA-2.1, JRA-2.2, JRA-2.3)
Augmented by negotiation-related concepts
Considering UI aspects; e.g., user models ( JRA-1.1)
Quality selection model: Specifies the importance of each quality attribute for the requester and how the best service should be selected.
Requestor’s Requirements towards QoS
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 43/<Max>
(c) Prof. Dr. Klaus Pohl

44. © S-Cube – 44/<Max>
Automatic quality contract establishment Foundation: Quality Definition D N
S-Cube Quality Meta Model (Excerpt) [1, 2, 3, CD-JRA-1.3.3]
Negotiation-related concepts …
Negotiable: provider can fix the QoS value at execution time (e.g., response time)
Non-negotiable: QoS value is pre-determined at execution time (e.g., reputation)
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 44/<Max>
(c) Prof. Dr. Klaus Pohl

45. © S-Cube – 45/<Max>
Automatic quality contract establishment Results: Negotiation Techniques D N
Framework for quality (QoS) contracting [1, CD-JRA-1.3.3]
(1) Phase 1: Matchmaking
a) Service offer must cover the requirements on non-negotiable QoS dimensions
b) Service offer must cover, at least partially, the requirements on negotiable QoS dimensions expressed
c) Price associated to minimum quality profile  budget B of service requestor
(2) Phase 2: Selection
Providers ranked by bidding function
Maximize requirement coverage (maintaining price below B)
Penalize services that only partially cover the requirements (utility function)
Provider of service associated to lowest bid L is selected
Extra budget EB = B – L
(3) Phase 3: “Actual” Negotiation (if EB > 0)
Select for each negotiable QoS dimension a single QoS value
Assuming EB should be spent (user model)
Increase of QoS levels (order relation) based on priority of QoS attributes (quality selection model)
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 45/<Max>
(c) Prof. Dr. Klaus Pohl

46. Run-time Quality Assurance Techniques
Problems
Design-time QA is not enough due to dynamic adaptation, context changes and open nature of SBAs
Monitoring at run-time only checks “arbitrary” applications in operation (no systematic coverage)
Solution
Understanding when in the life-cycle run-time verification should be performed
Exploiting consolidated design-time QA techniques (here: verification) during run-time
S-Cube Publications
Domenico Bianculli, Carlo Ghezzi and Cesare Pautasso. Embedding Continuous Lifelong Verification in Service Life Cycles. In Proceedings ICSE 2009
Domenico Bianculli and Carlo Ghezzi. SAVVY-WS at a glance: supporting verifiable dynamic service compositions. In Proceedings ASE 2008
Domenico Bianculli, Carlo Ghezzi, Paola Spoletini, Luciano Baresi and Sam Guinea. A Guided Tour through SAVVY-WS: a Methodology for Specifying and Validating Web Service Compositions. In Proceedings Advances in Software Engineering 2008
Andreas Gehlert, Antonio Bucchiarone, Raman Kazhamiakin, Andreas Metzger, Marco Pistore, Klaus Pohl: Exploiting Assumption-Based Verification for the Adaptation of Service-Based Applications. In Proceedings SAC 2010
Brice Morin, Olivier Barais, Grégory Nain, and Jean-Marc Jézéquel. Taming dynamically adaptive systems with models and aspects. In Proceedings ICSE 2009.
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 46/<Max>
(c) Prof. Dr. Klaus Pohl

47. Run-time Quality Assurance Techniques Results
Life-cycle Model [1, CD-JRA-1.3.4]
QA oriented life cycle layered on existing iterative life cycle ( WP-JRA-1.1)
Due to open nature, SBS need to "continuously" assert properties that have a “lifelong” validity
E.g., there is no guarantee that a service implementation eventually fulfils the contract promised (e.g., SLA)
E.g., during design-time QA, it is not possible to model the behaviour of the underlying distributed infrastructure (e.g., Internet)
Existing QA techniques applied at each stage of the service life cycle
Combining different techniques can improve the overall quality of QA
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 47/<Max>
(c) Prof. Dr. Klaus Pohl

48. © S-Cube – 48/<Max>
Run-time Quality Assurance Techniques Results A
Run-time Verification: Basic Approach [2, 3, 4]
Activities during Design-Time:
Specify service composition (workflow): W ( JRA-1.1, JRA-2.2)
Assume properties of the outside world / context: A ( Negotiation)
e.g., QoS of services as stipulated in SLAs
Formalize requirements towards workflow: R ( JRA-1.1, JRA-2.2)
Check (e.g., using model checker) that workflow meets requirements
W, A |-- R ? W R A X
W, A |-- R ?
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 48/<Max>
(c) Prof. Dr. Klaus Pohl 48

49. © S-Cube – 49/<Max>
Run-time Quality Assurance Techniques Results A
Run-time Verification: Basic Approach [2, 3, 4, CD-JRA-1.3.4]
Activities during Run-time:
(1) Monitor assumptions ( JRA-1.2): M
(2) Check violation of assumptions: M  A ?
If violated: (3) check if requirements are still met
based on past monitoring data M + assumptions on “future” invocations A’
W, A’, M |-- R
If requirements are not met: (4) adapt SBA (e.g., replace services) ( JRA-1.2) X M
M  A ?  W, A’, M |-- R ?
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 49/<Max>
(c) Prof. Dr. Klaus Pohl 49

50. Quality Prediction Techniques to Support Proactive Adaptation
Problems
Reactive adaptation has significant shortcomings
Quality prediction needed to enable pro-active adaptation ( JRA-1.2)
Unnecessary pro-active adaptations need to be avoided (as they can be costly)
Solution
Exploiting synergies between monitoring and online testing
Determining (and fostering) confidence of failure prediction
S-Cube Publications
Julia Hielscher, Raman Kazhamiakin, Andreas Metzger and Marco Pistore. A Framework for Proactive Self-Adaptation of Service-based Applications Based on Online Testing. In ServiceWave 2008, Nr. (5377), Springer, December 2008.
Andreas Gehlert, Julia Hielscher, Olha Danylevych and Dimka Karastoyanova. Online Testing, Requirements Engineering and Service Faults as Drivers for Adapting Service Compositions. In Dimka Karastoyanova, Raman Kazhamiakin, Andreas Metzger and Marco Pistore editors, Proceedings of the International Workshop on Service Monitoring, Adaptation and Beyond (MONA+ 2008), December 13, 2008, Madrid, Spain, Pages , 2008.
Andreas Gehlert, Andreas Metzger, Dimka Karastoyanova, Raman Kazhamiakin, Klaus Pohl, Frank Leymann, Marco Pistore. Adaptation of Service-Based Systems based on Requirements Engineering and Online Testing. Internet of Services Book (S. Dustdar, Ed.) – to be published -
Andreas Metzger, Osama Sammodi, Klaus Pohl. Towards Pro-Active Adaptation with Confidence – Augmenting Monitoring with Online Testing. SEAMS ICSE 2010
Philipp Leitner, Branimir Wetzstein, Florian Rosenberg, Anton Michlmayr, Schahram Dustdar, and Frank Leymann. Runtime Prediction of Service Level Agreement Violations for Composite Services. In Proceedings Non-Functional Properties and SLA ICSOC 2009
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 50/<Max>
(c) Prof. Dr. Klaus Pohl

51. Quality Prediction Techniques to Support Proactive Adaptation
PROSA-Framework [1, 2, 3, CD-JRA-1.3.4]
Framework for exploiting online testing for pro-active adaptation
Integration of online testing with monitoring
Integration of pro-active, corrective adaptation with pro-active, perfective adaptation
Failure Prediction with Confidence [4, CD-JRA-1.3.4]
Determining whether failure prediction is of expected confidence
Initiating online tests to collect data points for required confidence
Invalidating data points in cases of adaptation
Service-based Application
Decide on
Adaptation
monitor
adapt
predict
Online
Testing
A. Metzger / WP-JRA-1.3 – Global Meeting, Pisa, Mar. 2010
© S-Cube – 51/<Max>
(c) Prof. Dr. Klaus Pohl

52. Case studies: a methodological approach

53. Towards validation: Case studies
Goals:
validate research results with realistic scenarios
develop research challenges derived from case studies
A methodology for case study documentation has been developed
(c) Prof. Dr. Klaus Pohl

54. Case studies S-Cube proposal
An approach to describe case studies derived from NEXOF-RA and enriched with other elements from the RE literature
The identification of a set of case studies which the approach is applied on
(c) Prof. Dr. Klaus Pohl

55. Case studies Directly from S-CUBE Derived from NEXOF-RA
Wine production (Donnafugata)
Automotive supply chain (360Fresh and IBM)
Derived from NEXOF-RA
E-health diagnostic workflow (Siemens/Thales)
Traffic management (Siemens)
E-government (TIS and Engineering)
For a complete description of the case studies analyzed in S-Cube, please refer to the deliverable CD-IA and for scenarios CD-IA-2.2.4
© S-Cube
(c) Prof. Dr. Klaus Pohl
(c) Prof. Dr. Klaus Pohl 55

56. The proposed case study description approach
Business goals: express the main purposes of some system in the terms of the business domain in which the system will live or currently lives
Domain assumptions and constraints: report properties of the domain or restrictions on the design of the system architecture
Domain description: phenomena occurring in the world together with the laws that regulate such a world
Abstract scenario description: a way to describe world phenomena
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
© S-Cube
(c) Prof. Dr. Klaus Pohl 56

57. Business goals and domain assumptions/constraints
Business goals and domain assumption/constraints rely on the same elements:
Description
Rationale
Involved stakeholders
Conflicts
Supporting material
Priority
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
© S-Cube
(c) Prof. Dr. Klaus Pohl 57

58. Domain description Purpose Content
Study and describe phenomena in the world as well as shared phenomena
Content
Glossary
Relationships among the main terms
Through class diagrams, semantic networks, E/R diagrams, …
Boundaries between the world and the machine
Context diagrams
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
© S-Cube
(c) Prof. Dr. Klaus Pohl 58

59. Scenarios description
Purpose: to describe possible situations and interactions between the world and the machine
Structure of description
Involved actors
Detailed operational description
Problems and challenges
Non-functional requirements and constraints
Accompanying material
sequence and activity diagrams
(sub)use case diagrams
From scenarios, abstract scenarios are derived (template)
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
© S-Cube
(c) Prof. Dr. Klaus Pohl 59

60. Case study description life-cycle
The four elements NOT necessarily have to be defined sequentially
Goalsassumptionsdomainscenario
They can be defined iteratively
Some rules:
All the terms used in the description have to be put in a glossary
All identified actors have to appear in the context diagram (and vice versa)
From each scenario there exist at least one related business goal and vice versa
Scenarios are not overlapping
Goals are not overlapping
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
© S-Cube
(c) Prof. Dr. Klaus Pohl

61. Coverage of life cycle
Barbara Pernici – Month 24 Review Meeting, April 2010
(c) Prof. Dr. Klaus Pohl
(c) Prof. Dr. Klaus Pohl 61

62. Classifying & Comparing case studies
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
© S-Cube
(c) Prof. Dr. Klaus Pohl
(c) Prof. Dr. Klaus Pohl 62

63. Case study meta-data
Used to index case studies in the repository for facilitating search mechanisms
Meta-data:
Source
Real vs. Realistic
Abstract
Available solutions
Licensing …
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
© S-Cube
(c) Prof. Dr. Klaus Pohl

64. Comparison dimensions
S-Cube
Description of business situations and presence of agile service networks
Need for negotiating, establishing, monitoring, enforcing QoS
Need for service consumers with various different characteristics
Need for distributed infrastructures
Need for highly distributed service compositions
Highly changing requirements at various levels (from business to infrastructure)
Others
Security
Reliability …
P. Plebani - IE4SOC Opening - Stockholm 23/11/2009
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(c) Prof. Dr. Klaus Pohl 64

65. SEEKING NEW CASE STUDIES (OR SCENARIOS FOR EXISTING ONES)!
(c) Prof. Dr. Klaus Pohl