SlideShare uma empresa Scribd logo
1 de 20
Baixar para ler offline
Problem Context

SODA: A Tool Support for the Detection of SOA
Antipatterns
Mathieu Nayrolles1,3, Francis Palma 1,2, Naouel Moha1, Yann-Gaël Guéhéneuc2

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
Problem Context (1/2)
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
2
Francis Palma,

Specification and Detection of SOA Antipatterns
Problem Context (2/2)

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
3
Francis Palma,

Specification and Detection of SOA Antipatterns
Introduction: Contribution
With the goal to detect SOA Antipatterns in SBSs:

- SODA (Service Oriented Detection for Antipatterns), a

SODA Tool

novel and innovative approach.

- SOFA (Service Oriented Framework for Antipatterns), a

SODA
Approach

framework,
- to specify SOA antipatterns and detect them automatically
- to perform static and dynamic analysis

SOFA
Framework

- SODA tool to perform detection using a Graphical User Interface (GUI).
4
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA: Our Approach

Rule
Card

SBS
Detection
Algorithm

3. Detection

Templat
e

2. Generation

Textual
Description of
Antipatterns

1. Specifications

SODA: Service Oriented Detection for Antipatterns

Suspicious
Services

5
Francis Palma,

Specification and Detection of SOA Antipatterns
SOFA: Underlying Framework

Main Components:
(1) Automated generation of detection algorithms
(2) Computation of static and dynamic metrics
(3) Specification of rules
6
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA Tool

7
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA Tool: Principal Features

- Direct import of an SBS as a Jar package.
- Straight forward detection interface for the
users, handy both for beginners and experts.
- Expose all the detection details, i.e., metric values, corresponding rule
cards, textual descriptions of antipatterns etc.
- Supports both well-known metric based and execution trace based
analysis of SBSs.
- Exposes all the execution traces, association rules generated from
those traces, and relations among them, that is also useful to the users to
better understand the SBS analyzed.
8
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA Tool (1/7)

Enlists the SOA
antipatterns
that can be
detected.

9
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA Tool (2/7)
Textual
description
for selected
antipattern.

10
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA Tool (3/7)

Corresponding
rule card.

11
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA Tool (4/7)

Results,
i.e., suspicious
service(s)

12
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA Tool (5/7)

Values for all
metrics (from the
associated rule
card), for each
service

13
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA Tool (6/7)

Exposes the
generated
association
rules.

14
Francis Palma,

Specification and Detection of SOA Antipatterns
SODA Tool (7/7)

Helps to visualize
the suspicious
service(s) within
the analyzed SBS.

15
Francis Palma,

Specification and Detection of SOA Antipatterns
Results (1/3): Detection
Detection of Bottleneck Service: Mediator and Patient DAO

16
Francis Palma,

Specification and Detection of SOA Antipatterns
Results (2/3): Detection
Detection of Duplicated Service: Mediator and Communication

17
Francis Palma,

Specification and Detection of SOA Antipatterns
Results (3/3): Detection
Detection of Multiservice: Mediator

18
Francis Palma,

Specification and Detection of SOA Antipatterns
Conclusion

19
Francis Palma,

Specification and Detection of SOA Antipatterns
Future Work

- Add more SOA antipatterns to SOFA
- Develop an Eclipse plugin to support SOA antipatterns detection
- Adapt other technologies, e.g., RESTful services, SOAP services
in the SODA tool
- Introduce refactoring module in SODA

20
Francis Palma,

Specification and Detection of SOA Antipatterns

Mais conteúdo relacionado

Mais procurados

An Empirical Comparison of Model Validation Techniques for Defect Prediction ...
An Empirical Comparison of Model Validation Techniques for Defect Prediction ...An Empirical Comparison of Model Validation Techniques for Defect Prediction ...
An Empirical Comparison of Model Validation Techniques for Defect Prediction ...Chakkrit (Kla) Tantithamthavorn
 
Using Developer Information as a Prediction Factor
Using Developer Information as a Prediction FactorUsing Developer Information as a Prediction Factor
Using Developer Information as a Prediction FactorTim Menzies
 
Sound Empirical Evidence in Software Testing
Sound Empirical Evidence in Software TestingSound Empirical Evidence in Software Testing
Sound Empirical Evidence in Software TestingJaguaraci Silva
 
On the application of SAT solvers for Search Based Software Testing
On the application of SAT solvers for Search Based Software TestingOn the application of SAT solvers for Search Based Software Testing
On the application of SAT solvers for Search Based Software Testingjfrchicanog
 
Instance Space Analysis for Search Based Software Engineering
Instance Space Analysis for Search Based Software EngineeringInstance Space Analysis for Search Based Software Engineering
Instance Space Analysis for Search Based Software EngineeringAldeida Aleti
 
Speeding-up Software Testing With Computational Intelligence
Speeding-up Software Testing With Computational IntelligenceSpeeding-up Software Testing With Computational Intelligence
Speeding-up Software Testing With Computational IntelligenceAnnibale Panichella
 
Code-Review-COW56-Meeting
Code-Review-COW56-MeetingCode-Review-COW56-Meeting
Code-Review-COW56-MeetingMasud Rahman
 
Towards a Better Understanding of the Impact of Experimental Components on De...
Towards a Better Understanding of the Impact of Experimental Components on De...Towards a Better Understanding of the Impact of Experimental Components on De...
Towards a Better Understanding of the Impact of Experimental Components on De...Chakkrit (Kla) Tantithamthavorn
 
Automated parameter optimization should be included in future 
defect predict...
Automated parameter optimization should be included in future 
defect predict...Automated parameter optimization should be included in future 
defect predict...
Automated parameter optimization should be included in future 
defect predict...Chakkrit (Kla) Tantithamthavorn
 
Using the Machine to predict Testability
Using the Machine to predict TestabilityUsing the Machine to predict Testability
Using the Machine to predict TestabilityMiguel Lopez
 
Model based test case prioritization using neural network classification
Model based test case prioritization using neural network classificationModel based test case prioritization using neural network classification
Model based test case prioritization using neural network classificationcseij
 
Feature Selection Techniques for Software Fault Prediction (Summary)
Feature Selection Techniques for Software Fault Prediction (Summary)Feature Selection Techniques for Software Fault Prediction (Summary)
Feature Selection Techniques for Software Fault Prediction (Summary)SungdoGu
 
Dg3 j35 electronicfaultfinding [1]
Dg3 j35 electronicfaultfinding [1]Dg3 j35 electronicfaultfinding [1]
Dg3 j35 electronicfaultfinding [1]Rajesh Mallela
 
A practical guide for using Statistical Tests to assess Randomized Algorithms...
A practical guide for using Statistical Tests to assess Randomized Algorithms...A practical guide for using Statistical Tests to assess Randomized Algorithms...
A practical guide for using Statistical Tests to assess Randomized Algorithms...Lionel Briand
 
AI-Driven Software Quality Assurance in the Age of DevOps
AI-Driven Software Quality Assurance in the Age of DevOpsAI-Driven Software Quality Assurance in the Age of DevOps
AI-Driven Software Quality Assurance in the Age of DevOpsChakkrit (Kla) Tantithamthavorn
 
Complexity Measures for Secure Service-Orieted Software Architectures
Complexity Measures for Secure Service-Orieted Software ArchitecturesComplexity Measures for Secure Service-Orieted Software Architectures
Complexity Measures for Secure Service-Orieted Software ArchitecturesTim Menzies
 
Automated exam question set generator using utility based agent and learning ...
Automated exam question set generator using utility based agent and learning ...Automated exam question set generator using utility based agent and learning ...
Automated exam question set generator using utility based agent and learning ...Journal Papers
 
AI in SE: A 25-year Journey
AI in SE: A 25-year JourneyAI in SE: A 25-year Journey
AI in SE: A 25-year JourneyLionel Briand
 

Mais procurados (20)

An Empirical Comparison of Model Validation Techniques for Defect Prediction ...
An Empirical Comparison of Model Validation Techniques for Defect Prediction ...An Empirical Comparison of Model Validation Techniques for Defect Prediction ...
An Empirical Comparison of Model Validation Techniques for Defect Prediction ...
 
Using Developer Information as a Prediction Factor
Using Developer Information as a Prediction FactorUsing Developer Information as a Prediction Factor
Using Developer Information as a Prediction Factor
 
Sound Empirical Evidence in Software Testing
Sound Empirical Evidence in Software TestingSound Empirical Evidence in Software Testing
Sound Empirical Evidence in Software Testing
 
On the application of SAT solvers for Search Based Software Testing
On the application of SAT solvers for Search Based Software TestingOn the application of SAT solvers for Search Based Software Testing
On the application of SAT solvers for Search Based Software Testing
 
Instance Space Analysis for Search Based Software Engineering
Instance Space Analysis for Search Based Software EngineeringInstance Space Analysis for Search Based Software Engineering
Instance Space Analysis for Search Based Software Engineering
 
Wcre13a.ppt
Wcre13a.pptWcre13a.ppt
Wcre13a.ppt
 
Speeding-up Software Testing With Computational Intelligence
Speeding-up Software Testing With Computational IntelligenceSpeeding-up Software Testing With Computational Intelligence
Speeding-up Software Testing With Computational Intelligence
 
Code-Review-COW56-Meeting
Code-Review-COW56-MeetingCode-Review-COW56-Meeting
Code-Review-COW56-Meeting
 
Wcre13b.ppt
Wcre13b.pptWcre13b.ppt
Wcre13b.ppt
 
Towards a Better Understanding of the Impact of Experimental Components on De...
Towards a Better Understanding of the Impact of Experimental Components on De...Towards a Better Understanding of the Impact of Experimental Components on De...
Towards a Better Understanding of the Impact of Experimental Components on De...
 
Automated parameter optimization should be included in future 
defect predict...
Automated parameter optimization should be included in future 
defect predict...Automated parameter optimization should be included in future 
defect predict...
Automated parameter optimization should be included in future 
defect predict...
 
Using the Machine to predict Testability
Using the Machine to predict TestabilityUsing the Machine to predict Testability
Using the Machine to predict Testability
 
Model based test case prioritization using neural network classification
Model based test case prioritization using neural network classificationModel based test case prioritization using neural network classification
Model based test case prioritization using neural network classification
 
Feature Selection Techniques for Software Fault Prediction (Summary)
Feature Selection Techniques for Software Fault Prediction (Summary)Feature Selection Techniques for Software Fault Prediction (Summary)
Feature Selection Techniques for Software Fault Prediction (Summary)
 
Dg3 j35 electronicfaultfinding [1]
Dg3 j35 electronicfaultfinding [1]Dg3 j35 electronicfaultfinding [1]
Dg3 j35 electronicfaultfinding [1]
 
A practical guide for using Statistical Tests to assess Randomized Algorithms...
A practical guide for using Statistical Tests to assess Randomized Algorithms...A practical guide for using Statistical Tests to assess Randomized Algorithms...
A practical guide for using Statistical Tests to assess Randomized Algorithms...
 
AI-Driven Software Quality Assurance in the Age of DevOps
AI-Driven Software Quality Assurance in the Age of DevOpsAI-Driven Software Quality Assurance in the Age of DevOps
AI-Driven Software Quality Assurance in the Age of DevOps
 
Complexity Measures for Secure Service-Orieted Software Architectures
Complexity Measures for Secure Service-Orieted Software ArchitecturesComplexity Measures for Secure Service-Orieted Software Architectures
Complexity Measures for Secure Service-Orieted Software Architectures
 
Automated exam question set generator using utility based agent and learning ...
Automated exam question set generator using utility based agent and learning ...Automated exam question set generator using utility based agent and learning ...
Automated exam question set generator using utility based agent and learning ...
 
AI in SE: A 25-year Journey
AI in SE: A 25-year JourneyAI in SE: A 25-year Journey
AI in SE: A 25-year Journey
 

Destaque

Icsm07 tooldemo.pdf
Icsm07 tooldemo.pdfIcsm07 tooldemo.pdf
Icsm07 tooldemo.pdfPtidej Team
 
Software Design Patterns in Theory
Software Design Patterns in TheorySoftware Design Patterns in Theory
Software Design Patterns in TheoryPtidej Team
 
Quality and Software Design Patterns
Quality and Software Design PatternsQuality and Software Design Patterns
Quality and Software Design PatternsPtidej Team
 
AsianPLoP'14: How and Why Design Patterns Impact Quality and Future Challenges
AsianPLoP'14: How and Why Design Patterns Impact Quality and Future ChallengesAsianPLoP'14: How and Why Design Patterns Impact Quality and Future Challenges
AsianPLoP'14: How and Why Design Patterns Impact Quality and Future ChallengesPtidej Team
 
Software Design Patterns in Practice
Software Design Patterns in PracticeSoftware Design Patterns in Practice
Software Design Patterns in PracticePtidej Team
 

Destaque (16)

Wcre12b.ppt
Wcre12b.pptWcre12b.ppt
Wcre12b.ppt
 
Ssbse12a.ppt
Ssbse12a.pptSsbse12a.ppt
Ssbse12a.ppt
 
Mribp13.ppt
Mribp13.pptMribp13.ppt
Mribp13.ppt
 
Icsm07 tooldemo.pdf
Icsm07 tooldemo.pdfIcsm07 tooldemo.pdf
Icsm07 tooldemo.pdf
 
Wcre12c.ppt
Wcre12c.pptWcre12c.ppt
Wcre12c.ppt
 
Ppap13b.ppt
Ppap13b.pptPpap13b.ppt
Ppap13b.ppt
 
Wcre13c.pdf
Wcre13c.pdfWcre13c.pdf
Wcre13c.pdf
 
Ppap13a.ppt
Ppap13a.pptPpap13a.ppt
Ppap13a.ppt
 
Rsse12.ppt
Rsse12.pptRsse12.ppt
Rsse12.ppt
 
See12.ppt
See12.pptSee12.ppt
See12.ppt
 
MSR Asia Summit
MSR Asia SummitMSR Asia Summit
MSR Asia Summit
 
Software Design Patterns in Theory
Software Design Patterns in TheorySoftware Design Patterns in Theory
Software Design Patterns in Theory
 
Quality and Software Design Patterns
Quality and Software Design PatternsQuality and Software Design Patterns
Quality and Software Design Patterns
 
AsianPLoP'14: How and Why Design Patterns Impact Quality and Future Challenges
AsianPLoP'14: How and Why Design Patterns Impact Quality and Future ChallengesAsianPLoP'14: How and Why Design Patterns Impact Quality and Future Challenges
AsianPLoP'14: How and Why Design Patterns Impact Quality and Future Challenges
 
Software Design Patterns in Practice
Software Design Patterns in PracticeSoftware Design Patterns in Practice
Software Design Patterns in Practice
 
Jcom02.ppt
Jcom02.pptJcom02.ppt
Jcom02.ppt
 

Semelhante a Icsoc12 tooldemo.ppt

Specification and Detection of SOA Antipatterns
Specification and Detection of SOA AntipatternsSpecification and Detection of SOA Antipatterns
Specification and Detection of SOA AntipatternsFrancis Palma
 
Specification and Detection of SOA Antipatterns in Web Services
Specification and Detection of SOA Antipatterns in Web ServicesSpecification and Detection of SOA Antipatterns in Web Services
Specification and Detection of SOA Antipatterns in Web ServicesFrancis Palma
 
Investigating the Change-proneness of Service Patterns and Antipatterns
Investigating the Change-proneness of Service Patterns and AntipatternsInvestigating the Change-proneness of Service Patterns and Antipatterns
Investigating the Change-proneness of Service Patterns and AntipatternsFrancis Palma
 
MingLiuResume2016
MingLiuResume2016MingLiuResume2016
MingLiuResume2016Ming Liu
 
10 years in Network Protocol testing L2 L3 L4-L7 Tcl Python Manual and Automa...
10 years in Network Protocol testing L2 L3 L4-L7 Tcl Python Manual and Automa...10 years in Network Protocol testing L2 L3 L4-L7 Tcl Python Manual and Automa...
10 years in Network Protocol testing L2 L3 L4-L7 Tcl Python Manual and Automa...Mullaiselvan Mohan
 
Network simulator
Network simulatorNetwork simulator
Network simulatorjausmin kj
 
Automatic Analyzing System for Packet Testing and Fault Mapping
Automatic Analyzing System for Packet Testing and Fault MappingAutomatic Analyzing System for Packet Testing and Fault Mapping
Automatic Analyzing System for Packet Testing and Fault MappingIRJET Journal
 
Juan Figueroa_Summary
Juan Figueroa_SummaryJuan Figueroa_Summary
Juan Figueroa_Summaryjuan figueroa
 
Crypto Mark Scheme for Fast Pollution Detection and Resistance over Networking
Crypto Mark Scheme for Fast Pollution Detection and Resistance over NetworkingCrypto Mark Scheme for Fast Pollution Detection and Resistance over Networking
Crypto Mark Scheme for Fast Pollution Detection and Resistance over NetworkingIRJET Journal
 
Basic Network Support Certification
Basic Network Support CertificationBasic Network Support Certification
Basic Network Support CertificationVskills
 
Project in malware analysis:C2C
Project in malware analysis:C2CProject in malware analysis:C2C
Project in malware analysis:C2CFabrizio Farinacci
 

Semelhante a Icsoc12 tooldemo.ppt (20)

Icsoc12 tooldemo.ppt
Icsoc12 tooldemo.pptIcsoc12 tooldemo.ppt
Icsoc12 tooldemo.ppt
 
Specification and Detection of SOA Antipatterns
Specification and Detection of SOA AntipatternsSpecification and Detection of SOA Antipatterns
Specification and Detection of SOA Antipatterns
 
Icsoc12.ppt
Icsoc12.pptIcsoc12.ppt
Icsoc12.ppt
 
Ecsa14.ppt
Ecsa14.pptEcsa14.ppt
Ecsa14.ppt
 
Ecsa14.ppt
Ecsa14.pptEcsa14.ppt
Ecsa14.ppt
 
Specification and Detection of SOA Antipatterns in Web Services
Specification and Detection of SOA Antipatterns in Web ServicesSpecification and Detection of SOA Antipatterns in Web Services
Specification and Detection of SOA Antipatterns in Web Services
 
Soca14.ppt
Soca14.pptSoca14.ppt
Soca14.ppt
 
Soca14.ppt
Soca14.pptSoca14.ppt
Soca14.ppt
 
Investigating the Change-proneness of Service Patterns and Antipatterns
Investigating the Change-proneness of Service Patterns and AntipatternsInvestigating the Change-proneness of Service Patterns and Antipatterns
Investigating the Change-proneness of Service Patterns and Antipatterns
 
ICSOC12.ppt
ICSOC12.pptICSOC12.ppt
ICSOC12.ppt
 
MingLiuResume2016
MingLiuResume2016MingLiuResume2016
MingLiuResume2016
 
Robin Singh-Fd
Robin Singh-FdRobin Singh-Fd
Robin Singh-Fd
 
10 years in Network Protocol testing L2 L3 L4-L7 Tcl Python Manual and Automa...
10 years in Network Protocol testing L2 L3 L4-L7 Tcl Python Manual and Automa...10 years in Network Protocol testing L2 L3 L4-L7 Tcl Python Manual and Automa...
10 years in Network Protocol testing L2 L3 L4-L7 Tcl Python Manual and Automa...
 
LVTS Projects
LVTS ProjectsLVTS Projects
LVTS Projects
 
Network simulator
Network simulatorNetwork simulator
Network simulator
 
Automatic Analyzing System for Packet Testing and Fault Mapping
Automatic Analyzing System for Packet Testing and Fault MappingAutomatic Analyzing System for Packet Testing and Fault Mapping
Automatic Analyzing System for Packet Testing and Fault Mapping
 
Juan Figueroa_Summary
Juan Figueroa_SummaryJuan Figueroa_Summary
Juan Figueroa_Summary
 
Crypto Mark Scheme for Fast Pollution Detection and Resistance over Networking
Crypto Mark Scheme for Fast Pollution Detection and Resistance over NetworkingCrypto Mark Scheme for Fast Pollution Detection and Resistance over Networking
Crypto Mark Scheme for Fast Pollution Detection and Resistance over Networking
 
Basic Network Support Certification
Basic Network Support CertificationBasic Network Support Certification
Basic Network Support Certification
 
Project in malware analysis:C2C
Project in malware analysis:C2CProject in malware analysis:C2C
Project in malware analysis:C2C
 

Mais de Ptidej Team

From IoT to Software Miniaturisation
From IoT to Software MiniaturisationFrom IoT to Software Miniaturisation
From IoT to Software MiniaturisationPtidej Team
 
Presentation by Lionel Briand
Presentation by Lionel BriandPresentation by Lionel Briand
Presentation by Lionel BriandPtidej Team
 
Manel Abdellatif
Manel AbdellatifManel Abdellatif
Manel AbdellatifPtidej Team
 
Azadeh Kermansaravi
Azadeh KermansaraviAzadeh Kermansaravi
Azadeh KermansaraviPtidej Team
 
CSED - Manel Grichi
CSED - Manel GrichiCSED - Manel Grichi
CSED - Manel GrichiPtidej Team
 
Cristiano Politowski
Cristiano PolitowskiCristiano Politowski
Cristiano PolitowskiPtidej Team
 
Will io t trigger the next software crisis
Will io t trigger the next software crisisWill io t trigger the next software crisis
Will io t trigger the next software crisisPtidej Team
 
Thesis+of+laleh+eshkevari.ppt
Thesis+of+laleh+eshkevari.pptThesis+of+laleh+eshkevari.ppt
Thesis+of+laleh+eshkevari.pptPtidej Team
 
Thesis+of+nesrine+abdelkafi.ppt
Thesis+of+nesrine+abdelkafi.pptThesis+of+nesrine+abdelkafi.ppt
Thesis+of+nesrine+abdelkafi.pptPtidej Team
 

Mais de Ptidej Team (20)

From IoT to Software Miniaturisation
From IoT to Software MiniaturisationFrom IoT to Software Miniaturisation
From IoT to Software Miniaturisation
 
Presentation
PresentationPresentation
Presentation
 
Presentation
PresentationPresentation
Presentation
 
Presentation
PresentationPresentation
Presentation
 
Presentation by Lionel Briand
Presentation by Lionel BriandPresentation by Lionel Briand
Presentation by Lionel Briand
 
Manel Abdellatif
Manel AbdellatifManel Abdellatif
Manel Abdellatif
 
Azadeh Kermansaravi
Azadeh KermansaraviAzadeh Kermansaravi
Azadeh Kermansaravi
 
Mouna Abidi
Mouna AbidiMouna Abidi
Mouna Abidi
 
CSED - Manel Grichi
CSED - Manel GrichiCSED - Manel Grichi
CSED - Manel Grichi
 
Cristiano Politowski
Cristiano PolitowskiCristiano Politowski
Cristiano Politowski
 
Will io t trigger the next software crisis
Will io t trigger the next software crisisWill io t trigger the next software crisis
Will io t trigger the next software crisis
 
MIPA
MIPAMIPA
MIPA
 
Thesis+of+laleh+eshkevari.ppt
Thesis+of+laleh+eshkevari.pptThesis+of+laleh+eshkevari.ppt
Thesis+of+laleh+eshkevari.ppt
 
Thesis+of+nesrine+abdelkafi.ppt
Thesis+of+nesrine+abdelkafi.pptThesis+of+nesrine+abdelkafi.ppt
Thesis+of+nesrine+abdelkafi.ppt
 
Medicine15.ppt
Medicine15.pptMedicine15.ppt
Medicine15.ppt
 
Qrs17b.ppt
Qrs17b.pptQrs17b.ppt
Qrs17b.ppt
 
Icpc11c.ppt
Icpc11c.pptIcpc11c.ppt
Icpc11c.ppt
 
Icsme16.ppt
Icsme16.pptIcsme16.ppt
Icsme16.ppt
 
Msr17a.ppt
Msr17a.pptMsr17a.ppt
Msr17a.ppt
 
Icsoc15.ppt
Icsoc15.pptIcsoc15.ppt
Icsoc15.ppt
 

Último

Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsNathaniel Shimoni
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rick Flair
 
What is Artificial Intelligence?????????
What is Artificial Intelligence?????????What is Artificial Intelligence?????????
What is Artificial Intelligence?????????blackmambaettijean
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfLoriGlavin3
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxBkGupta21
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionDilum Bandara
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 

Último (20)

Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directions
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...
 
What is Artificial Intelligence?????????
What is Artificial Intelligence?????????What is Artificial Intelligence?????????
What is Artificial Intelligence?????????
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdf
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptx
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An Introduction
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 

Icsoc12 tooldemo.ppt

  • 1. Problem Context SODA: A Tool Support for the Detection of SOA Antipatterns Mathieu Nayrolles1,3, Francis Palma 1,2, Naouel Moha1, Yann-Gaël Guéhéneuc2 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
  • 2. Problem Context (1/2) 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 2 Francis Palma, Specification and Detection of SOA Antipatterns
  • 3. Problem Context (2/2) 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 3 Francis Palma, Specification and Detection of SOA Antipatterns
  • 4. Introduction: Contribution With the goal to detect SOA Antipatterns in SBSs: - SODA (Service Oriented Detection for Antipatterns), a SODA Tool novel and innovative approach. - SOFA (Service Oriented Framework for Antipatterns), a SODA Approach framework, - to specify SOA antipatterns and detect them automatically - to perform static and dynamic analysis SOFA Framework - SODA tool to perform detection using a Graphical User Interface (GUI). 4 Francis Palma, Specification and Detection of SOA Antipatterns
  • 5. SODA: Our Approach Rule Card SBS Detection Algorithm 3. Detection Templat e 2. Generation Textual Description of Antipatterns 1. Specifications SODA: Service Oriented Detection for Antipatterns Suspicious Services 5 Francis Palma, Specification and Detection of SOA Antipatterns
  • 6. SOFA: Underlying Framework Main Components: (1) Automated generation of detection algorithms (2) Computation of static and dynamic metrics (3) Specification of rules 6 Francis Palma, Specification and Detection of SOA Antipatterns
  • 7. SODA Tool 7 Francis Palma, Specification and Detection of SOA Antipatterns
  • 8. SODA Tool: Principal Features - Direct import of an SBS as a Jar package. - Straight forward detection interface for the users, handy both for beginners and experts. - Expose all the detection details, i.e., metric values, corresponding rule cards, textual descriptions of antipatterns etc. - Supports both well-known metric based and execution trace based analysis of SBSs. - Exposes all the execution traces, association rules generated from those traces, and relations among them, that is also useful to the users to better understand the SBS analyzed. 8 Francis Palma, Specification and Detection of SOA Antipatterns
  • 9. SODA Tool (1/7) Enlists the SOA antipatterns that can be detected. 9 Francis Palma, Specification and Detection of SOA Antipatterns
  • 10. SODA Tool (2/7) Textual description for selected antipattern. 10 Francis Palma, Specification and Detection of SOA Antipatterns
  • 11. SODA Tool (3/7) Corresponding rule card. 11 Francis Palma, Specification and Detection of SOA Antipatterns
  • 12. SODA Tool (4/7) Results, i.e., suspicious service(s) 12 Francis Palma, Specification and Detection of SOA Antipatterns
  • 13. SODA Tool (5/7) Values for all metrics (from the associated rule card), for each service 13 Francis Palma, Specification and Detection of SOA Antipatterns
  • 14. SODA Tool (6/7) Exposes the generated association rules. 14 Francis Palma, Specification and Detection of SOA Antipatterns
  • 15. SODA Tool (7/7) Helps to visualize the suspicious service(s) within the analyzed SBS. 15 Francis Palma, Specification and Detection of SOA Antipatterns
  • 16. Results (1/3): Detection Detection of Bottleneck Service: Mediator and Patient DAO 16 Francis Palma, Specification and Detection of SOA Antipatterns
  • 17. Results (2/3): Detection Detection of Duplicated Service: Mediator and Communication 17 Francis Palma, Specification and Detection of SOA Antipatterns
  • 18. Results (3/3): Detection Detection of Multiservice: Mediator 18 Francis Palma, Specification and Detection of SOA Antipatterns
  • 19. Conclusion 19 Francis Palma, Specification and Detection of SOA Antipatterns
  • 20. Future Work - Add more SOA antipatterns to SOFA - Develop an Eclipse plugin to support SOA antipatterns detection - Adapt other technologies, e.g., RESTful services, SOAP services in the SODA tool - Introduce refactoring module in SODA 20 Francis Palma, Specification and Detection of SOA Antipatterns