Specification and Detection of SOA Antipatterns in Web Services

Specification and Detection of SOA Antipatterns
in Web Services
Francis Palma, Naouel Moha, Guy Tremblay, and Yann-Gaël Guéhéneuc.
ECSA 2014, Vienna, Austria.

Problem Context (1/3)
HTTP, SOAP, WSDL,
UDDI, WS-Technologies
• Service-based systems composed of Web services evolve:
- degrade design, quality of service
- cause the appearance of common poor solutions: antipatterns
- antipatterns might exist since the beginning
• Antipatterns are ineffective solutions and have negative consequences
[A. Koenig, 1995].
Specification and Detection of SOA Antipatterns in Web Services
Service Consumer
Service Provider
Service-based system
2

Examples of Antipatterns in Web services:
Fine Grained Web Service*
3
God Object Web Service*
 Many Methods
 Very Low Cohesion
 High Response Time
 Low Availability
 Few Methods
 Low Cohesion
 Very High Coupling
* J2EE AntiPatterns, Bill Dudney, Stephen Asbury, Joseph K. Krozak, Kevin Wittkopf, ISBN: 978-0-471-14615-5, August 2003

An automatic approach to detect SOA antipatterns in Web services may
support their easy maintenance and evolution, and high reusability.
Poor performance
Hard for comprehension
Low maintainability
Hard for evolution
Expensive maintenance life
Low reusability
4
+

Outline
- Our Contribution
- Related Work
- Details on our Approach (SODA-W)
- Validation

Contribution
Goal: To assess the design and quality of service of Web services
• SODA-W (Service Oriented Detection for Antipatterns in Web services), a novel
and innovative approach
- inspired from SODA (Service Oriented Detection for Antipatterns), an approach
to detect antipatterns in SCA (Service Component Architecture) systems
• SOFA (Service Oriented Framework for Antipatterns), a framework,
- specifies SOA antipatterns and detect them automatically
- analyses service interfaces syntactically and semantically
- performs static and dynamic analysis
5Specification and Detection of SOA Antipatterns in Web Services

Related Work on Web Services (1/2)
Specification and Detection of SOA Antipatterns in Web Services 6
Heuristics-based, Rodriguez et al.

Rule cards-based SODA, Moha et al.

Guidelines to improve service
descriptions & search queries
EasySOC, Rodriguez et al.

Correlation analysis between OO & WSDL metrics,
Coscia et al.
2013
2009
Survey on service cohesion,
coupling, and reusability metrics,
Sindhgatta et al.

Survey on service cohesion,
coupling, and reusability metrics,
Sindhgatta et al.
Correlation analysis between OO & WSDL metrics,
Coscia et al.
- Specific SCA technology
- No semantic analysis
- Static analysis of WSDLs
- No antipatterns specification
- No analysis of design
- Static analysis of WSDLs
- No specification of antipatterns
- No design analysis
- No dedicated detection approach
- No design analysis
- No dedicated detection approach
2013
2009

Approach (1/7): SODA-W
SODA-W: Service Oriented Detection for Antipatterns in Web services
- inspired from SODA (Service Oriented Detection for Antipatterns)

Approach (2/7): Specification
1. Specify SOA Antipatterns
in Web services
Domain Analysis
Domain Specific
Language
Rule Cards of
Antipatterns
God Object Web Service corresponds to a Web service that
contains a large number of operations related to different
business abstractions. Often the client interactions break due to
frequent changes in the Web service definition, hence cause low
availability. This antipattern affects the reusability because the
operations are very low cohesive. Moreover, being overloaded
with a multitude of operations, this antipattern may also result
in high response time [Dudney et al. 2003].
BNF Grammar

Approach (3/7): Domain Specific Language

Approach (4/7): Example Rule Cards
God Object Web Service:
1 RULE_CARD: GodObjectWebService {
2 RULE: GodObjectWebService { INTER LowCohesion MultiOperation HighRT LowA };
3 RULE: LowCohesion { COH VERY_LOW };
4 RULE: MultiOperation { NOD HIGH };
5 RULE: HighRT { RT VERY_HIGH };
6 RULE: LowA { A LOW };
7 };
Fine Grained Web Service:
1 RULE_CARD: FineGrainedWebService {
2 RULE: FineGrainedWebService { INTER FewOperation HighCoupling LowCohesion };
3 RULE: FewOperation { NOD LOW };
4 RULE: HighCoupling { CPL VERY_HIGH };
5 RULE: LowCohesion { COH LOW };
6 };

Approach (5/7): Generation

Approach (5/7): Generation (on going)
Model Driven Engineering (MDE)
Parsing
1
Visiting &
Replacing
2
Templates
Models of Rule Cards

Approach (6/7): Detection
SOFA: Service Oriented Framework for Antipatterns

Approach (7/7): Underlying Framework

Experiments (1/5): Setup
 Purpose is to show,
- the generality of our DSL
- the preciseness of our detection algorithms, and
- the extensibility of our DSL and SOFA framework
 Subjects:
- 10 common SOA antipatterns in Web services
 Objects:
- 13 weather-related Web services
- 109 finance-related Web services

Experiments (2/5): Hypotheses
H1. Generality
Our DSL allows the specification of various SOA antipatterns, from simple to more
complex ones
H2. Accuracy
The detection algorithms have an average precision of more than 75% and a recall of
100%, i.e., more than three-quarters of detected antipatterns are true positive and we
do not miss any existing antipatterns
H3. Extensibility
Our DSL and SOFA framework are extensible for adding new metrics and new SOA
antipatterns

Experiments (3/5): Subjects
We specify 10 SOA antipatterns:
17
1. Ambiguous Name
2. Chatty Web Service
3. CRUDy Interface
4. Data Web Service
5. Duplicated Web Service
6. Fine Grained Web Service
7. God Object Web Service
8. Low Cohesive Operations in the Same PortType
9. Maybe It's Not RPC
10. Redundant PortTypes
Ambiguous Name
Chatty Web Service
 Low Cohesion
 High Accessor Methods
 High Methods
 Very Long or Short Signatures
 Too Many General Terms
 Verbs in Message Names
 Multiple Verbs in Operation Signatures

Experiments (4/5): Objects
Web service search engines:
 eil.cs.txstate.edu/ServiceXplorer
 programmableweb.com
 myexperiment.org
 taverna.org.uk, etc.
18
The set of Web services includes:
1. 13 weather-related Web services (keyword ‘Weather’)
2. 109 finance-related Web services (keyword ‘Finance’)
The complete list of analysed Web services is available on sofa.uqam.ca/soda-w/

Experiments (5/5): Process
1. Specify 10 Web services-specific SOA antipatterns using rule cards
2. Generate detection algorithm for each antipattern
3. Apply these algorithms on 122 Web services
4. Validate detection results by analysing the suspicious services manually
5. Use precision, recall, and specificity for accuracy measurement

Outline
- Our Contribution
- Related Work
- Details on our Approach (SODA-W)
- Validation Results

Results (1/6): Detection
20
RULE_CARD: AmbiguousName {
RULE: AmbiguousName {INTER GeneralTerm ShortORLongSignature VerbedMessage MultiVerbedOperation};
RULE: ShortORLongSignature {UNION ShortSignature LongSignature};
RULE: LongSignature {ALS VERY_HIGH};
RULE: ShortSignature {ALS VERY_LOW};
RULE: GeneralTerm {RGTS HIGH};
RULE: VerbedMessage {NVMS > 0};
RULE: MultiVerbedOperation {NVOS > 1};
};
Detection of Ambiguous Name antipattern in AIP3_PV_Impact Web service.
 Very Long or Short Signatures
 Too Many General Terms
 Verbs in Message Names
 Multiple Verbs in Operation Signatures

Results (2/6): Detection
31
Detection of Chatty Web Service antipattern in TaarifCustoms Web service.
RULE_CARD: ChattyWebService {
RULE: ChattyWebService {INTER LowCohesion HighDataAccessor MultiOperation LowPerformance};
RULE: LowCohesion {COH LOW};
RULE: HighDataAccessor {ANAO VERY_HIGH};
RULE: MultiOperation {NOD HIGH};
RULE: LowPerformance {INTER HighRT LowA};
RULE: HighRT {RT HIGH};
RULE: LowA {A LOW};
};
• Response time is calculated using SAAJ1 API and SoapUI2.
(1) https://saaj.java.net/ (2) www.soapui.org
23
 Low Cohesion
 High Accessor Methods
 High Methods

Results (3/6): Hypothesis H1
H1. Generality: Our DSL allows the specification of various SOA antipatterns, from
simple to more complex ones
1 RULE_CARD: LowCohesiveOperations {
2 RULE: LowCohesiveOperations { INTER MultiOperation LowCohesivePT };
4 RULE: LowCohesivePT { ARIO LOW };
5 };

simple to more complex ones
22
1 RULE_CARD: CRUDyInterface {
2 RULE: CRUDyInterface { INTER ChattyInterface HighCRUDOperation };
3 RULE: ChattyInterface { RULE_CARD: ChattyWebService };
4 RULE: HighCRUDOperation { NCO > 1 };
5 };
1 RULE_CARD: ChattyWebService {
2 RULE: ChattyWebService { INTER LowCohesion HighDataAccessor
MultiOperation LowPerformance };
4 RULE: HighDataAccessor { ANAO VERY_HIGH };
6 RULE: LowPerformance { INTER HighRT LowA };
7 RULE: HighRT { RT HIGH };
9 };
5 };

simple to more complex ones ...YES!!!
1 RULE_CARD: CRUDyInterface {
2 RULE: CRUDyInterface { INTER ChattyInterface HighCRUDOperation };
3 RULE: ChattyInterface { RULE_CARD: ChattyWebService };
4 RULE: HighCRUDOperation { NCO > 1 };
5 };
1 RULE_CARD: ChattyWebService {
2 RULE: ChattyWebService { INTER LowCohesion HighDataAccessor
MultiOperation LowPerformance };
4 RULE: HighDataAccessor { ANAO VERY_HIGH };
6 RULE: LowPerformance { INTER HighRT LowA };
7 RULE: HighRT { RT HIGH };
9 };
5 };

H2. Accuracy: The detection algorithms have an average precision of more than 75%
and a recall of 100%, i.e., more than three-quarters of detected antipatterns are true
positive and we do not miss any existing antipatterns
23
Experiments
Detection
Times
Detected
(True Positive)
Manually
Validated
Precision Recall Specificity
13 Weather
Web services
102.19s
7
(6)
6 85.71% 100% 98%
109 Finance
Web services
192.91s
24
(22)
22 91.67% 100% 99%
Average - - - 88.69% 100% 98.5%

H2. Accuracy: The detection algorithms have an average precision of more than 75%
and a recall of 100%, i.e., more than three-quarters of detected antipatterns are true
positive and we do not miss any existing antipatterns ...YES!!!
23
Experiments
Detection
Times
Detected
(True Positive)
Manually
Validated
Precision Recall Specificity
13 Weather
Web services
102.19s
7
(6)
6 85.71% 100% 98%
109 Finance
Web services
192.91s
24
(22)
22 91.67% 100% 99%
Average - - - 88.69% 100% 98.5%

H3. Extensibility: Our DSL and SOFA framework are extensible for adding new
metrics and new SOA antipatterns

metrics and new SOA antipatterns
24
- 10 SCA-specific SOA antipatterns
using SODA
- 10 Web service-specific SOA
antipatterns using SODA-W
Moreover, SOFA supports
the detection of,

metrics and new SOA antipatterns ...YES!!!
- 10 SCA-specific SOA antipatterns
using SODA
- 10 Web service-specific SOA
antipatterns using SODA-W
Moreover, SOFA supports
the detection of,

Results (6/6): Validation Summary
 Specify and implement detection algorithms for 10 Web service-specific SOA
antipatterns
 Detect 10 Web service-specific SOA antipatterns on two sets of Web services
 13 weather-related Web services
 109 finance-related Web services
 Average precision of our detection algorithms is 88.69% and recall is 100%
 Average specificity of our rule cards is 98.5%
 Detail results and more materials on sofa.uqam.ca/soda-w/

Conclusion

Future Work
- Additional experiments with more Web services and SOA antipatterns
- Correction through adding recommendation support for service developers
- Focus on other SOA styles and technologies, e.g., RESTful services

Related Work (1/2)
SOAOO
- Adopt OO design style
- Services as first-class entities
- Highly dynamic, also static
- Adopt procedural design style
- Classes as first-class entities
- Mainly static, also dynamic analysis
Books/Articles:
• J2EE Antipatterns, Dudney et al. 2003
• SOA Patterns, Rotem-Gal-Oz et al. 2012
• Crucial SO Antipatterns, Král and Žemlička, 2003
Detection Methods in OO:
• Kessentini et al. (2011), Munro et al. (2005), Settas et al. (2011), ...
► OO detection methods and tools cannot be directly applied to SOA:

52
RULE CARD: DataWebService {
RULE: DataWebService {INTER HighCohesion PrimitiveParameter HighAccessor LowParameter};
RULE: HighCohesion {COH HIGH};
RULE: PrimitiveParameter {ANPT HIGH};
RULE: HighAccessor {ANAO HIGH};
RULE: LowParameter {ANP LOW};
};
RULE CARD: LowCohesiveOperations {
RULE: LowCohesiveOperations {INTER MultiOperation LowCohesivePT};
RULE: LowCohesivePT {ARIO LOW};
};
RULE CARD: AmbiguousName {
RULE: AmbiguousName {INTER GeneralTerm ShortORLongSignature VerbedMessage MultiVerbedOperation};
RULE: ShortORLongSignature {UNION ShortSignature LongSignature};
RULE: LongSignature {ALS VERY HIGH};
RULE: ShortSignature {ALS VERY LOW};
RULE: GeneralTerm {RGTS HIGH};
RULE: VerbedMessage {NVMS > 0};
RULE: MultiVerbedOperation {NVOS > 1};
};
RULE CARD: DuplicatedService {
RULE: DuplicatedWebService {INTER IdenticalPortType IdenticalOperation};
RULE: IdenticalPortType {ARIP HIGH};
RULE: IdenticalOperation {ARIO HIGH};
};

53
RULE CARD: ChattyWebService {
RULE: ChattyWebService {INTER LowCohesion HighDataAccessor MultiOperation LowPerformance};
RULE: LowCohesion {COH LOW};
RULE: HighDataAccessor {ANAO VERY HIGH};
RULE: LowPerformance {INTER HighRT LowA};
RULE: LowA {A LOW};
};
RULE CARD: CRUDyInterface {
RULE: CRUDyInterface {INTER ChattyInterface HighCRUDOperation};
RULE: ChattyInterface { RULE CARD: ChattyWebService};
RULE: HighCRUDOperation {NCO > 1};
};
RULE CARD: RedundantPortType {
RULE: RedundantPortType {INTER MultiPortType HighCohesivePT};
RULE: MultiPortType {NPT > 1};
RULE: MultiOperations {NOPT > 1};
RULE: HighCohesivePT {ARIP VERY HIGH};
};
RULE CARD: MaybeItsNotRPC {
RULE: MaybeItsNotRPC {INTER HighRT HighCRUDOperation HighParameter};
RULE: HighCRUDOperation {NCO VERY HIGH};
RULE: HighParameter {ANP HIGH};
};

55
Comparison with State-of-the-art approaches

Threats to Validity
 External Validity:
Generalisation to other large set of Web services
 Internal Validity:
Detection depends on services provided by SOFA . We experimented on a
representative set of Web service-specific antipatterns
 Construct Validity:
Rule cards are subjective. We define rule cards based on thorough literature review
and domain analysis

Related Work (1/2)
Books/Articles:
• J2EE Antipatterns, Dudney et al. 2003
• SOA Patterns, Rotem-Gal-Oz et al. 2012
• Crucial SO Antipatterns, Král and Žemlička, 2003
Detection Methods in OO:
• Kessentini et al. (2011), Munro et al. (2005), Settas et al. (2011), ...

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