1. Specification and Detection of SOA Antipatterns
Francis Palma1,2
In collaboration with
Naouel Moha1, Mathieu Nayrolles 1, Benjamin Joyen Conseil 1,
Yann-Gaël Guéhéneuc 2.
1 LATECE , Département d'informatique, Université du Québec à Montréal, Canada
2 Ptidej Team, DGIGL, École Polytechnique de Montréal, Canada

2. Problem Context (1/3)
Service-based System
Service Requester
HTTP, SOAP, WSDL, UDDI,
WS-Technologies Service Provider
Service Based Systems (SBSs) evolve to fit new user requirements, execution
contexts:
- may degrade design and quality of service (QoS)
- may cause the appearance of common poor solutions:
Antipatterns
- Antipatterns hinder the future maintenance and evolution of SBSs
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Francis Palma, Specification and Detection of SOA Antipatterns

3. Problem Context (2/3)
Tiny Service
Multiservice
Examples of SOA Antipatterns:
Tiny Service: Small service with few methods which requires several
coupled services to complete an abstraction
Multiservice: Implements a multitude of methods, is not easily reusable
because of low cohesion of its methods, is often unavailable due to overload
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Francis Palma, Specification and Detection of SOA Antipatterns

4. Problem Context (3/3)
results
Service-based (or any)
Systems with Antipatterns
Thus, automatic detection of SOA Antipatterns in SBSs requires special attention
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Francis Palma, Specification and Detection of SOA Antipatterns

5. Outline
- Introduction
- Related Work
- Approach
- Experiments/Results
- Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

6. Introduction: Contribution
With the goal to assess the design and QoS of SBSs:
- SODA (Service Oriented Detection for Antipatterns), a novel and
innovative approach
- SOFA (Service Oriented Framework for Antipatterns), a framework,
- to specify SOA antipatterns and detect them automatically
- to perform static and dynamic analysis
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Francis Palma, Specification and Detection of SOA Antipatterns

7. Outline
- Introduction
- Related Work
- Approach
- Experiments/Results
- Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

8. Related Work (1/2)
OO SOA
• Several books: • Only 2 books:
Books Brown 1998, Fowler 1999 Dudney 2003, Rotem-Gal-
Oz 2012
• Numerous mature methods: • Still in their infancy:
Detection Munro 2005, Lanza 2006, Kral 2008
Methods Moha 2010, Kessentini 2010,
Settas 2011
Other • Web sites, courses, • Many Web sites of SOA
references magazines, journals etc. practitioners
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Francis Palma, Specification and Detection of SOA Antipatterns

9. Related Work (2/2)
OO SOA
• Procedural design style • OO style design in SOA
Root cause
in OO system system
First class
• Classes • Services
entities
Analysis • Mainly static • Highly dynamic
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Francis Palma, Specification and Detection of SOA Antipatterns

10. Related Work (2/2)
OO SOA
• Procedural design style • OO style design in SOA
Root cause
in OO system system
First class
• Classes • Services
entities
Analysis • Mainly static • Highly dynamic
è OO detection methods, tools cannot be directly applied to SOA
è However, they form a sound basis of expertise and knowledge for
building methods for the detection of SOA antipatterns
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Francis Palma, Specification and Detection of SOA Antipatterns

11. Outline
- Introduction
- Related Work
- Approach
- Experiments/Results
- Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

12. Approach (1/9): SODA
SODA: Service Oriented Detection for Antipatterns
SBS
1. Specifications
2. Generation
3. Detection
Rule Detection
Textual
Card algorithm Suspicious
Description of
Antipatterns Services
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Francis Palma, Specification and Detection of SOA Antipatterns

13. Approach (2/9): Specification
SBS
1. Specifications
2. Generation
3. Detection
Textual Rule Card Detection Suspicious
1. Specify SOA Description of algorithm Services
Antipatterns
Antipatterns
Domain Analysis
BNF “Multi Service also known as God Object
corresponds to a service that implements a multitude
Domain Specific
Language of methods related to different business and technical
abstractions . This aggregates too much into a single
Rule Cards of service, such a service is not easily reusable because
Antipatterns
of the low cohesion of its methods and is often
unavailable to end -users because of its overload ,
which may induce a high response time”
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Francis Palma, Specification and Detection of SOA Antipatterns

14. Approach (3/9): Domain Specific Language
SBS
1. Specifications
2. Generation
3. Detection
Textual Rule Card Detection Suspicious
Description of algorithm Services
Antipatterns
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Francis Palma, Specification and Detection of SOA Antipatterns

15. Approach (4/9): Example Rule Cards
SBS
1. Specifications
2. Generation
3. Detection
Textual Rule Card Detection Suspicious
Description of algorithm Services
Antipatterns
Tiny Service
1 RULE CARD: TinyService {
2 RULE: TinyService { INTER FewMethod HighCoupling };
3 RULE: FewMethod { NMD VERY LOW };
4 RULE: HighCoupling { CPL HIGH };
5 };
Multi Service
1 RULE CARD: MultiService {
2 RULE: MultiService { INTER MultiMethod HighResponse LowAvailability LowCohesion };
3 RULE: MultiMethod { NMD VERY HIGH };
4 RULE: HighResponse { RT VERY HIGH };
5 RULE: LowAvailability { A LOW };
6 RULE: LowCohesion { COH LOW };
7 };
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Francis Palma, Specification and Detection of SOA Antipatterns

16. Approach (5/9): Generation
SBS
1. Specifications
2. Generation
3. Detection
Textual Rule Card Detection Suspicious
Description of algorithm Services
Antipatterns
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Francis Palma, Specification and Detection of SOA Antipatterns

17. Approach (6/9): Detection
SBS
1. Specifications
2. Generation
3. Detection
Textual Rule Card Detection Suspicious
Description of algorithm Services
Antipatterns
SOFA
Framework
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Francis Palma, Specification and Detection of SOA Antipatterns

18. Approach (7/9): Underlying Framework
Main Components:
(1) Automated generation of detection algorithms
(2) Computation of static and dynamic metrics
(3) Specification of rules
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Francis Palma, Specification and Detection of SOA Antipatterns

19. Approach (8/9): Underlying Framework
Main Components:
(1) Automated generation of detection algorithms
(2) Computation of static and dynamic metrics
(3) Specification of rules
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Francis Palma, Specification and Detection of SOA Antipatterns

20. Approach (9/9): Underlying Framework
Main Components:
(1) Automated generation of detection algorithms
(2) Computation of static and dynamic metrics
(3) Specification of rules
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Francis Palma, Specification and Detection of SOA Antipatterns

21. Outline
- Introduction
- Related Work
- Approach
- Experiments/Results
- Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

22. Experiments (1/5): Setup
Purpose:
To validate the effectiveness of generated detection algorithms and the
usefulness of SODA
Expected Results:
§ DSL allows us to specify SOA antipatterns
§ Detection algorithms have a recall of 100% and an accuracy more than 75%
§ The detection times are the order of few seconds
Subjects: 10 SOA antipatterns
Objects: 2 versions of Home-Automation
Process: Precision and recall on Home-Automation (v1.0 and v1.1)
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Francis Palma, Specification and Detection of SOA Antipatterns

23. Experiments (2/5): Assumptions
A1. Completeness: The DSL allows the specification of many different SOA
antipatterns, from simple to more complex ones
A2. Accuracy: The generated detection algorithms have a recall of 100%,
and a precision greater than 75%
A3. Extensibility: The DSL and the SOFA framework are extensible for
adding new SOA antipatterns
A4. Performance: The computation time required for the detection of
antipatterns using the generated algorithms is reasonably very low (few
seconds)
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Francis Palma, Specification and Detection of SOA Antipatterns

24. Experiments (2/5): Assumptions
A1. Completeness: The DSL allows the specification of many different SOA
antipatterns, from simple to more complex ones
A2. Accuracy: The generated detection algorithms have a recall of 100%,
and a precision greater than 75%
A3. Extensibility: The DSL and the SOFA framework are extensible for
adding new SOA antipatterns
A4. Performance: The computation time required for the detection of
antipatterns using the generated algorithms is reasonably very low (few
seconds)
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Francis Palma, Specification and Detection of SOA Antipatterns

25. Experiments (2/5): Assumptions
A1. Completeness: The DSL allows the specification of many different SOA
antipatterns, from simple to more complex ones
A2. Accuracy: The generated detection algorithms have a recall of 100%,
and a precision greater than 75%
A3. Extensibility: The DSL and the SOFA framework are extensible for
adding new SOA antipatterns
A4. Performance: The computation time required for the detection of
antipatterns using the generated algorithms is reasonably very low (few
seconds)
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Francis Palma, Specification and Detection of SOA Antipatterns

26. Experiments (2/5): Assumptions
A1. Completeness: The DSL allows the specification of many different SOA
antipatterns, from simple to more complex ones
A2. Accuracy: The generated detection algorithms have a recall of 100%,
and a precision greater than 75%
A3. Extensibility: The DSL and the SOFA framework are extensible for
adding new SOA antipatterns
A4. Performance: The computation time required for the detection of
antipatterns using the generated algorithms is reasonably very low (few
seconds)
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Francis Palma, Specification and Detection of SOA Antipatterns

27. Experiments (3/5): Subjects
- Apply SODA using SOFA framework to specify 10 SOA antipatterns
- 7 SOA antipatterns from literature, 3 newly defined
Service Chain
Bottleneck Service
The Knot
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Francis Palma, Specification and Detection of SOA Antipatterns

28. Experiments (4/5): Objects
Two different versions of Home-Automation:
- Original version with 13 services
- A version modified by adding & modifying services to inject Antipatterns
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Francis Palma, Specification and Detection of SOA Antipatterns

29. Experiments (5/5):Process
1. Use SOFA to generate detection algorithms for corresponding rule cards
2. Apply these algorithms on Home-Automation
3. Validate detection results by analyzing the suspicious services manually:
- Validate Suspicious Services as True Positives (TP)
- Identify False Negatives (FN)
q Use Precision (P) and Recall (R) for accuracy measurement
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Francis Palma, Specification and Detection of SOA Antipatterns

30. Outline
- Introduction
- Related Work
- Approach
- Experiments/Results
- Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

31. Results (1/8): Detection
Detection of Bottleneck Service: Mediator and Patient DAO
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Francis Palma, Specification and Detection of SOA Antipatterns

32. Results (2/8): Detection
Detection of Duplicated Service: Mediator and Communication
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Francis Palma, Specification and Detection of SOA Antipatterns

33. Results (3/8): Detection
Detection of Multiservice: Mediator
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Francis Palma, Specification and Detection of SOA Antipatterns

34. Results (4/8): Assumption A1
A1. Completeness: The DSL allows the specification of many different SOA
antipatterns, from simple to more complex ones.
Bottleneck Service
The Knot Sand Pile
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35. Results (4/8): Assumption A1
A1. Completeness: The DSL allows the specification of many different SOA antipatterns, from simple to more complex
ones.
Therefore, any SOA antipattern from
simpler to more complex ones can be
defined using our DSL
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Francis Palma, Specification and Detection of SOA Antipatterns

36. Results (5/8): Assumption A2
A2. Accuracy: The generated detection algorithms have a recall of 100%, i.e., all existing antipatterns are detected, and
a precision greater than 75%.
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Francis Palma, Specification and Detection of SOA Antipatterns

37. Results (5/8): Assumption A2
A2. Accuracy: The generated detection algorithms have a recall of 100%, i.e., all existing antipatterns are detected, and
a precision greater than 75%.
Our detection algorithms have high
Precision and Recall…
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Francis Palma, Specification and Detection of SOA Antipatterns

38. Results (6/8): Assumption A3
A3. Extensibility: The DSL and the SOFA framework are extensible for adding new SOA antipatterns.
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Francis Palma, Specification and Detection of SOA Antipatterns

39. Results (6/8): Assumption A3
A3. Extensibility: The DSL and the SOFA framework are extensible for adding new SOA antipatterns.
Our DSL and SOFA framework are
extensible for new SOA antipatterns…
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Francis Palma, Specification and Detection of SOA Antipatterns

40. Results (7/8): Assumption A4
A4. Performance: The computation time required for the detection of antipatterns using the generated algorithms is
reasonably very low.
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Francis Palma, Specification and Detection of SOA Antipatterns

41. Results (7/8): Assumption A4
A4. Performance: The computation time required for the detection of antipatterns using the generated algorithms is
reasonably very low.
Computation time required for detection using
our generated algorithms is reasonably low…
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Francis Palma, Specification and Detection of SOA Antipatterns

42. Results (8/8): Summary
q Specify and generate detection algorithms for 10 SOA antipatterns
q Detect 10 SOA antipatterns in Home-Automation
q Including 2 SOA antipatterns in modified version
q Precision of our detection algorithms is 92.5%, and Recall is 100%
q Detail results and more materials on sofa.uqam.ca
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Francis Palma, Specification and Detection of SOA Antipatterns

43. Threats to Validity
Ø External Validity
Ø Internal Validity: Detection depends on services provided by SOFA ,
experiment on a representative set of antipatterns
Ø Construct Validity: Subjective nature, define rule cards based on
literature review & domain analysis
Ø Reliability Validity: Automate the generation of detection algorithm;
subsequent detection produce consistent sets of results
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Francis Palma, Specification and Detection of SOA Antipatterns

44. Outline
- Introduction
- Related Work
- Approach
- Experiments/Results
- Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

45. Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

46. Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

47. Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

48. Conclusion
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Francis Palma, Specification and Detection of SOA Antipatterns

49. Future Works: Part 1
- Add more SOA antipatterns to SOFA
- Replicate SODA approach on other SBSs
- Using industrial setup
- Increase the precision
- Introduce refactoring module within SOFA
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Francis Palma, Specification and Detection of SOA Antipatterns

50. Future Works: Part 2
- Association rules on execution traces
- Machine learning technique…
- Semantic analysis
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Francis Palma, Specification and Detection of SOA Antipatterns

51. Thank You
Questions ?
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Francis Palma, Specification and Detection of SOA Antipatterns

52. Algorithm Generation
Francis Palma, Specification and Detection of SOA Antipatterns

53. Algorithm Generation
Francis Palma, Specification and Detection of SOA Antipatterns