specification and detection of soa antipatternssofa.uqam.ca/media/icsoc2012phdsymp.pdfdetection of...

Detection of SOA Antipatterns

Francis Palma1,2

Supervised by Dr. Yann-Gaël Guéhéneuc1 and Dr. Naouel Moha2

1 Ptidej Team, DGIGL, École Polytechnique de Montréal, Canada

2 LATECE , Département d'informatique, Université du Québec à Montréal, Canada

PhD Symposium, ICSOC 2012, Shanghai, China

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 Detection of SOA Antipatterns

Service Requester

Service Provider

Service-based System

2


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 Detection of SOA Antipatterns 3


results

► SOA

→ next evolutionary step for IT organizations to meet up their business needs [IBM04]

→ current and future programming model, an architectural choice

→ ever demanding and increasing

Thus, SOA applications and SOA developments are being matured

Detection of SOA Antipatterns in SBSs requires special attention


Poor performance

Hard for comprehension

Low maintainability

Hard for evolution

Expensive maintenance life

Not reusable

4

+

Outline

- Introduction

- Related Work

- Approach

- Experiments/Results

- Conclusion


Introduction: My PhD Subject (1/2)

Current Progress:

« Detecting Object-oriented Code smells, Design smells and

Antipatterns are matured enough »

1. Detection of SOA Antipatterns

- Specification of SOA Antipatterns

- Devise an Approach and a Framework supporting detection

- Diverse technologies supporting SBSs

- Develop a complete tool for detection

2. Validation of the Detection Results

- User studies (validation of the tool, detected results) Francis Palma Detection of SOA Antipatterns 6


Problem(s) to Tackle:

« Propose an Approach for the Detection of SOA Antipatterns »




- Diverse technologies supporting SBSs



- User studies (validation of the tool, detected results)








- Supporting diverse technologies used in SBSs






Proposed Solution:

- SODA (Service Oriented Detection for Antipatterns), generic approach for

detecting SOA antipatterns [Moha et al. ICSOC 2012]

- SOFA (Service Oriented Framework for Antipatterns), a framework,

- to specify SOA antipatterns and detect them

- to perform static and dynamic analysis

- A complete Detection Tool on top of SODA and SOFA


Research Challenges

(a) Specifying SOA Antipatterns

- Current literature of SOA antipatterns is not matured enough

- Mostly depend on online resources, i.e., open forums, blogs, shared resources

(b) Repository of SBSs

- The research on the detection of SOA antipatterns still did not gain much attention

- No repository of SBSs to perform experiments. Researchers are still in the

lack of freely available SBSs as testbed

(c) Handling Dynamic Environment of SOA

- SBSs are developed adopting SOA design principles, and the execution

environment of SBSs is dynamic and unpredictable (many operating factors, e.g.,

implementation technologies, inter-operability, QoS, critical design criteria)


Outline

- Introduction

- Related Work

- Approach


- Conclusion


Related Work (1/2)

SOA 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


Related Work (2/2)

SOA OO

• OO style design in SOA

system

• Procedural design style

in OO system Root cause

• Services • Classes First class

entities

• Highly dynamic • Mainly static Analysis


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

SOA OO

• OO style design in SOA

system

• Procedural design style

in OO system Root cause

• Services • Classes First class

entities

• Highly dynamic • Mainly static Analysis


Other Related Work


Parsons and Murphy proposed a framework for detecting performance

antipatterns in component-based systems [Parsons08]

Cortellessa et al. proposed an approach to identify "guilty" performance

antipatterns by classifying the level of guiltiness in OO systems [Cortellessa10]

Trubiani et al. proposed detection and solution of software performance

antipatterns in domain specific modeling language to describe component-based

software architectures [Trubiani11]

Wong et al. used a genetic algorithm for detecting software faults and anomalous

behavior related to the resource usage of a system [Wong10]

Other works investigating impacts of antipatterns: Abbes et al. [Abbes11],

Khomh et al. [Khomh12]

Outline

- Introduction

- Related Work

- Proposed Approach


- Conclusion


Approach (1/6): SODA

SODA: Service Oriented Detection for Antipatterns

Textual

Description of

Antipatterns

1. S

pecif

icati

on

s

Rule

Card

2. G

en

era

tio

n

Detection

algorithm

3. D

ete

cti

on

Suspicious

Services


SBS Template

18

Approach (2/6): Specification

Textual Description of Antipatterns

1. S

pe

cifi

cati

on

s

Rule Card

2. G

en

era

tio

n

Detection algorithm

3. D

ete

ctio

n

Suspicious Services

1. Specify SOA Antipatterns

Domain Analysis

Domain Specific Language

Rule Cards of Antipatterns

Tiny Service is a small service with few methods,

which only implements part of an abstraction. Such

service often requires several coupled services to

be used together, resulting in higher development

complexity and reduced usability. In the extreme

case, a Tiny Service will be limited to one method,

resulting in many services that implement an overall

set of requirements [Dudney (2003)]

BNF Grammar


SBS

19

Approach (3/6): Example Rule Cards

Tiny Service


1. S

pe

cifi

cati

on

s

Rule Card

2. G

en

era

tio

n

Detection algorithm

3. D

ete

ctio

n

Suspicious Services

1 RULE CARD: TinyService {

2 RULE: TinyService { INTER FewMethod HighCoupling };

3 RULE: FewMethod { NMD VERY_LOW };

4 RULE: HighCoupling { CPL HIGH };

5 };


SBS

20

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 };

Approach (4/6): Generation


1. S

pe

cifi

cati

on

s

Rule Card

2. G

en

era

tio

n

Detection algorithm

3. D

ete

ctio

n

Suspicious Services


SBS

21

Approach (5/6): Detection

SOFA Framework


1. S

pe

cifi

cati

on

s

Rule Card

2. G

en

era

tio

n

Detection algorithm

3. D

ete

ctio

n

Suspicious Services


SBS

22

Approach (6/6): Underlying Framework

Main Components:

(1) Automated generation of detection algorithms

(2) Computation of static and dynamic metrics

(3) Specification of rules


1 2

3

Outline

- Introduction

- Related Work

- Approach

- Experiments/Assumptions

- Conclusion


Assumptions (1/2)

Assumption 1: The DSL allows specification from simple to complex SOA

antipatterns, i.e., DSL is generic (A1)

Assumption 2: The DSL is extensible for new SOA antipatterns (H12).

Assumption 3: Detection algorithms for SOA antipatterns have high precision

and recall. Target precision is above 75% and recall is 100% (H23).

Assumption 4: The complexity of the generated detection algorithms are

reasonable, i.e., execution times are in the order of seconds (H24).


Assumptions (1/2)



Assumption 2: The DSL is extensible for new SOA antipatterns (A2)


and recall. Target precision is above 75% and recall is 100% (H23).




Assumptions (1/2)





and recall. Target precision is above 75% and recall is 100% (A3)




Assumptions (1/2)





and recall. Target precision is above 75% and recall is 100% (A3)


reasonable, i.e., execution times are in the order of seconds (A4)


Assumptions (2/2)

Assumption 5: The proposed framework, SOFA is extensible for new SOA

metrics, and generic for any SBSs (A5)

Assumption 6: The proposed approach, SODA supports any technologies, i.e.,

SBSs developed using different technologies, e.g., SOAP/RPC, REST, Web

Services etc. (H46).

Assumption 7: The proposed approach is validated with different experimental

and control groups, to show its effectiveness, and to achieve precision and

recall closed to the experimental results (H47).


Assumptions (2/2)





Services, etc (A6)

Assumption 7: The proposed approach is validated with different experimental

and control groups, to show its effectiveness, and to achieve precision and

recall closed to the experimental results (H47).


Assumptions (2/2)





Services, etc (A6)

Assumption 7: The proposed approach is validated by means of a tool, to

show its effectiveness, and to achieve precision and recall closed to the

experimental results (A7)


Analyzed SOA Antipatterns


As a preliminary step, we analyzed 10 SOA antipatterns to specify

Rule Cards:

Multi Service

Tiny Service

Sand Pile

Chatty Service

The Knot

Nobody Home

Duplicated Service

Bottleneck Service

Service Chain

Data Service

Detection Result (Partial)

Detection for Tiny Service, Multi Service and Duplicated Service on Home

Automation.


Outline

- Introduction

- Related Work

- Approach


- Conclusion


Conclusion and Future Work

Conclusion

- Present the problem in detail

- The proposed possible solution

- The research assumptions to validate

- Detection result for 3 SOA antipatterns


Conclusion and Future Work

Future Work

- Add more SOA antipatterns to SOFA

- Replicate SODA approach on other large SBSs

- Focus on precision and recall for complex and large SBSs

- Focus on the performance of the detection algorithms

- Validate all the Assumptions (A1 to A7) for large scale SBS

- Introduce refactoring module within SOFA


References [Parsons08] Trevor Parsons and John Murphy, Detecting Performance Antipatterns in Component Based

Enterprise Systems, Journal of Object Technology, April 2008.

[Cortellessa10] Cortellessa, Vittorio and Martens, Anne and Reussner, Ralf and Trubiani, Catia, A Process to

Effectively Identify “Guilty” Performance Antipatterns, Fundamental Approaches to Software Engineering, 2010.

[Trubiani11] Trubiani, Catia and Koziolek, Anne, Detection and solution of software performance antipatterns in

palladio architectural models, Proceedings of the second joint WOSP/SIPEW international conference on

Performance engineering, 2011.

[Wong10] Wong, Sunny and Aaron, Melissa and Segall, Jeffrey and Lynch, Kevin and Mancoridis, Spiros, Reverse

Engineering Utility Functions Using Genetic Programming to Detect Anomalous Behavior in Software, WCRE '10.

[Abbes11] Abbes, Marwen and Khomh, Foutse and Gueheneuc, Yann-Gael and Antoniol, Giuliano, An Empirical

Study of the Impact of Two Antipatterns, Blob and Spaghetti Code, on Program Comprehension, Proceedings of

the 2011 15th European Conference on Software Maintenance and Reengineering, CSMR '11.

[Khomh12] Khomh, Foutse and Penta, Massimiliano Di and Gueheneuc, Yann-Gael and Antoniol, Giuliano, An

exploratory study of the impact of antipatterns on class change- and fault-proneness, June 2012.

[Dudney03] Dudney, B., Asbury, S., Krozak, J., Wittkopf, K.: J2EE AntiPatterns. John Wiley & Sons Inc (2003)

Thank You

specification and detection of soa antipatternssofa.uqam.ca/media/icsoc2012phdsymp.pdfdetection of...

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