SODA is a novel approach for specifying and detecting SOA antipatterns. It involves specifying antipatterns using a domain-specific language, generating detection algorithms from the specifications, and applying the algorithms to detect antipatterns in service-based systems. An evaluation on two versions of a home automation system found that SODA could accurately detect 10 different antipatterns, with a recall of 100% and precision over 75%. The approach demonstrates potential for automated analysis and improvement of service-oriented architectures.

1. Specification and Detection of SOA Antipatterns
Naouel Moha1, Francis Palma1,2, Mathieu Nayrolles1,3,
Benjamin Joyen Conseil1,3, Yann-Gaël Guéhéneuc2
Benoit Baudry4, and Jean-Marc Jézéquel4
1 LATECE , Département d'informatique, Université du Québec à Montréal, Canada
2 Ptidej Team, DGIGL, École Polytechnique de Montréal, Canada
3 CESI.eXia, Ecole Superieur d'Informatique, France
4INRIA Rennes, Université Rennes 1, France
ICSOC 2012, Shanghai, China
HOME

2. Problem Context (1/3)
HTTP, SOAP, WSDL, UDDI,
WS-Technologies
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
Antipattern is a pattern commonly used in software engineering but is ineffective and
negative consequences (Andrew Koenig, 1995)
Francis Palma Specification and Detection of SOA Antipatterns
Service Requester
Service Provider
Service-based System
2

3. Problem Context (2/3)
Examples of SOA Antipatterns:
Tiny Service: Small service with few methods which requires several coupled services
to complete an abstraction [Dudney03]
Multiservice: Implements a multitude of methods, is not easily reusable because of low
cohesion of its methods, is often unavailable due to overload [Dudney03]
Tiny Service Multiservice
Francis Palma Specification and Detection of SOA Antipatterns 3

4. Problem Context (3/3)
Antipatterns hinder the future maintenance and evolution of SBSs
Thus, automatic detection of SOA Antipatterns in SBSs requires special attention
Francis Palma Specification and Detection of SOA Antipatterns
Poor performance
Hard for comprehension
Low maintainability
Hard for evolution
Expensive maintenance life
Not reusable
4
+

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

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
Francis Palma Specification and Detection of SOA Antipatterns 6

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

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

9. Related Work (2/2)
 OO detection methods, tools cannot be directly applied to SOA.
 However, they form a sound basis of expertise and knowledge for
building methods for the detection of SOA antipatterns.
Francis Palma Specification and Detection of SOA Antipatterns
SOAObject Oriented (OO)
 OO style design in SOA
system
 Procedural design style in
OO system
Root cause
 Services Classes
First class
entities
 Highly dynamic Mainly staticAnalysis
9

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

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

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

13. Approach (2/9): Specification
Textual
Description of
Antipatterns
1.Specifications
Rule Card
2.Generation
Detection
algorithm
3.Detection
Suspicious
Services
1. Specify SOA
Antipatterns
Domain Analysis
Domain Specific
Language
Rule Cards of
Antipatterns
“Multi Service also known as God Object corresponds
to a service that implements a multitude of methods
related to different business and technical abstractions.
This aggregates too much into a single service, such a
service is not easily reusable because 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” [Dudney03]
BNF Grammar
Francis Palma Specification and Detection of SOA Antipatterns
SBSTemplate
13

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

15. Approach (4/9): Example Rule Cards
Multi Service :
Textual
Description of
Antipatterns
Rule Card
2.Generation
Detection
algorithm
3.Detection
Suspicious
Services
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 };
Francis Palma Specification and Detection of SOA Antipatterns
SBSTemplate
Tiny Service :
1 RULE CARD: TinyService {
2 RULE: TinyService { INTER FewMethod HighCoupling };
3 RULE: FewMethod { NMD VERY_LOW };
4 RULE: HighCoupling { CPL HIGH };
5 };
15
1.Specifications

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

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

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

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

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

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

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 precision more than 75%
 The detection times are the order of few seconds
Subjects: 10 SOA antipatterns
Objects: 2 versions of Home-Automation
Francis Palma Specification and Detection of SOA Antipatterns 22

23. Experiments (2/5): Assumptions
Francis Palma Specification and Detection of SOA Antipatterns
A1. Generality
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)
23

24. Experiments (2/5): Assumptions
Francis Palma Specification and Detection of SOA Antipatterns
A1. Generality
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)
24

25. Experiments (2/5): Assumptions
Francis Palma Specification and Detection of SOA Antipatterns
A1. Generality
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)
25

26. Experiments (2/5): Assumptions
A1. Generality
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)
Francis Palma Specification and Detection of SOA Antipatterns 26

27. Experiments (3/5): Subjects
- We specify 10 SOA antipatterns (7 from literature, 3 newly defined)
Bottleneck Service
The Knot
Service Chain
Francis Palma Specification and Detection of SOA Antipatterns 27
 Multi Service
 Tiny Service
 Sand Pile
 Chatty Service
 The Knot
 Nobody Home
 Duplicated Service
 Bottleneck Service
 Service Chain
 Data Service

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
Francis Palma Specification and Detection of SOA Antipatterns 28

29. 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
Francis Palma Specification and Detection of SOA Antipatterns 29

30. Experiments (5/5):Process
1. Specify 10 SOA antipatterns using rule cards
2. Use SOFA to generate detection algorithms for each antipatterns
3. Apply these algorithms on Home-Automation system
4. Validate detection results by analyzing the suspicious services manually
5. Use Precision and Recall for accuracy measurement
Francis Palma Specification and Detection of SOA Antipatterns 30

31. Outline
- Introduction
- Related Work
- Approach
- Experiments/Results
- Conclusion
Francis Palma Specification and Detection of SOA Antipatterns 31

32. Results (1/8): Detection
Detection of Bottleneck Service: Mediator and Patient DAO
Francis Palma Specification and Detection of SOA Antipatterns 32

33. Results (2/8): Detection
Detection of Duplicated Service: Mediator and Communication
Francis Palma Specification and Detection of SOA Antipatterns 33
*ANIM: Average Number of Identical Methods

34. Results (3/8): Detection
Detection of Multiservice: Mediator
Francis Palma Specification and Detection of SOA Antipatterns 34
* Availability 100% due to local service

35. Results (4/8): Assumption A1
A1. Generality: The DSL allows the specification of many different SOA antipatterns,
from simple to more complex ones
Bottleneck Service
The Knot
Sand Pile
35Francis Palma

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

37. Results (6/8): Assumption A3
A3. Extensibility: The DSL and the SOFA framework are extensible for adding new
SOA antipatterns
Francis Palma Specification and Detection of SOA Antipatterns 37

38. Results (7/8): Assumption A4
A4. Performance: The computation time required for the detection of antipatterns using
the generated algorithms is reasonably very low (few seconds)
Francis Palma Specification and Detection of SOA Antipatterns 38

39. Results (8/8): Summary
 Specify and generate detection algorithms for 10 SOA antipatterns
 Detect 10 SOA antipatterns in two versions of Home-Automation
 Precision of our detection algorithms is 92.5%, and Recall is 100%
 Detail results and more materials on sofa.uqam.ca
Francis Palma Specification and Detection of SOA Antipatterns 39

40. Threats to Validity
 External Validity: Generalization to other SBSs.
 Internal Validity: Detection depends on services provided by SOFA . We
experiment on a representative set of SOA antipatterns
 Construct Validity: Rule Cards are subjective. We define rule cards based on
literature review & domain analysis
 Reliability Validity: We automate the generation of detection algorithm.
Subsequent detection produce consistent sets of results
Francis Palma Specification and Detection of SOA Antipatterns

41. Outline
- Introduction
- Related Work
- Approach
- Experiments/Results
- Conclusion
Francis Palma Specification and Detection of SOA Antipatterns 41

42. Conclusion
Francis Palma Specification and Detection of SOA Antipatterns 42

43. Francis Palma Specification and Detection of SOA Antipatterns
Conclusion
43

44. Francis Palma Specification and Detection of SOA Antipatterns
Conclusion
44

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

46. Francis Palma Specification and Detection of SOA Antipatterns
Future Work
- Add more SOA antipatterns to SOFA
- Replicate SODA approach on other SBSs
- Focus on other technologies, e.g., RESTful services
- Increase detection precision
- Introduce correction module within SOFA
46

47. SODA tool demo (Demo Session 2)
When: Wednesday, 14 November 2012 (3:30pm - 4:50pm)
Where: No. 1 Meeting Room
Thank You… Questions ?

48. SODA tool demo (Demo Session 2)
When: Wednesday, 14 November 2012 (3:30pm - 4:50pm)
Where: No. 1 Meeting Room
Thank You… Questions ?

49. Francis Palma Specification and Detection of SOA Antipatterns 49

50. Algorithm Generation
Francis Palma Specification and Detection of SOA Antipatterns

51. Algorithm Generation
Francis Palma Specification and Detection of SOA Antipatterns