web service antipatterns detection using genetic programming

Software Engineering Laboratory, Department of Computer Science, Graduate School of Information Science and Technology, Osaka University

Web Service Antipatterns Detection Using

Genetic Programming

Ali Ouni1, Raula Gaikovina Kula1, Marouane Kessentini2, Katsuro Inoue1

1Osaka University, Japan2University of Michigan, USA

Genetic and Evolutionary Computation Conference (GECCO’15)

13 July 2015, Madrid, Spain

Department of Computer Science, Graduate School of Information Science and Technology, Osaka University

• SOA is

– a way of designing system

– an approach to system development

– a design paradigm

• SOA is not an architecture, is not a system

• SOA can be implemented utilizing different technologies

– OSGi, SCA, REST, Web services

• Service-based system = a set of ready-made, composable and reusable

services

What is SOA?


Services

• Services are reusable components that represent business tasks

– Customer lookup

– Meteo servie

– Localisation service

– Credit check

– Hotel reservation

– Interest calculation

• Services provide reusable functionality with well-defined interfaces

• An SOA infrastructure enables discovery, composition and invocation of services


• Example: Travel system

Service-based system

Client

Travel

Service

+ bookTrip()

Payment serviceFlight Service

+ checkDates()

+ reserveFlight()

+ cancelFlight()

Rent Car Service

+ checkAvailability()

+ reserveCar()

+ cancelCar()

HotelService

+ checkHotel()

+ reserveHotel()

+ cancelHotel()

XML technologies

{SOAP, WSDL, UDDI,...}

Service provider

Internet

Client

Client


If designed well…

• If designed well, Web service adoption can lead to

– Cost-efficiency

– interoperability

– Agility

– Adaptability

– Leverage of legacy investments

The hard part is the “if designed well”.


• SOA Change continuously– add new user requirements

– Adapt to environment changes

• Poorly planned changes may degrade their design quality

and QoS

–> bad design practices: Anti-patterns

Problem

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Typical Web service antipatterns

Multi-service Nano-service

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TravelService

Endpoint

+ bookFlight()

+ reserveFlight()

+ cancelFlight()

+ reserveCar()

+ cancelCar()

+ reserveHotel()

+ checkDates()

+ modifyBooking()

+ acceptPayment()

+ addPaymentDetails()

+ validateCredit()

+ printInvoice()

+ sendInvoice()

Client 1

Client 2

Client 3

Client n

Flight details.xml

Car reservation.xml

Hotel booking.xml

Payment.xml...

TravelService

Flight Reservation

Service

+ bookFlight()

Flight Change

Service

+ changeFlight()

Flight Cancellation

Service

+ cancelFlight()

- Symptoms

- Aggregates too many methods into a single

service

- Low cohesion of its methods

- Consequences

- Not easily reusable

- Overload: often unavailable to end-users

- Symptoms

- Small service with few operations

- whose overhead outweighs its utility

- Consequences

- requires several coupled services

- high development complexity

- reduced usability


Current approaches

• Manual approaches

– Service antipatterns definition (Dudney et al., 2003, Král et al, 2009, Rotem-

Gal-Oz et al., 2012)

• Symptoms-based approaches

– Detection rules (Moha et al., 2012, Palma et al., 2014)

• Translate antipattern symptoms into detection rules

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Antipattern detection challenges

• Difficult to define/express detection rules

– Large list of antipattern types to categorize

– Large exhaustive list of quality metrics

– Large number of possible threshold values

– Huge space to explore: An expert to manually write and validate

detection rules

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Idea: Infer detection rules from antipattern examples


Approach

Generation of

Web services anti-patterns

detection rules

(Genetic Programming)

Generation of

Web services anti-patterns

detection rules

(Genetic Programming)

Metric suite Web services anti-patterns detection

rules

Base of Examples (Web service anti-patterns)

category 2category 1

category 3

Anti-pattern instance


GP adaptation

NSTNOPT

COHNOD

[1..200]

[1..100]

Quality metrics

NCT

Solution representation

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1 : multi-service

Base of examples

antipattern

instance

IF (NOD(s)≥21 AND COH(s)≤0.32 AND NOPT(s)≥7.8) OR (NOD(s)≥24 AND COH(s)≤0.2 AND NPT(s)≥3 AND NST(s)≥41 OR NCT(s)≥32) THEN MultiService(s)


GP adaptation

• Fitness function

• Crossover

– random, single point crossover operator

• Mutation

– Substitution, deletion, insertion

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𝑓𝑖𝑡𝑛𝑒𝑠𝑠 =

𝑖=1𝑝𝑎𝑖

𝑡 + 𝑖=1𝑝𝑎𝑖

𝑝

2∈ [0,1]


Research questions

• RQ1 (SBSE Validation): How does the proposed GP-based

approach performs compared to RS, PSO and SA?

• RQ2 (Efficiency): To which extent can the proposed

approach detect Web service antipatterns?

• RQ3 (Sensitivity): What types of Web service antipatterns

does it detect correctly?

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Evaluation

• Studied Web service

– Benchmark of 310 Web services

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Category#

services

#

antipatterns

Average #

of operations

Average #

of messages

Average # of

complex types

Financial 94 67 29.52 57.31 19.01

Science 34 3 8.47 17.14 96.73

Search 37 13 8.35 18.94 26.13

Shipping 38 10 13.36 27.76 20.21

Travel 65 28 16.09 33.13 121.13

Weather 42 15 8.54 17.16 9.14


Evaluation

• Five common types of antipatterns– multiservice, nanoservice, chatty service, data service, and ambiguous service

• 6-fold cross validation– Detect antpatterns in a category using the 5 other categories

• Detection precision and recall

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𝑅𝑒𝑐𝑎𝑙𝑙 =𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠

𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑛𝑡𝑖𝑝𝑎𝑡𝑡𝑒𝑟𝑛𝑠

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠

𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝑎𝑛𝑡𝑖𝑝𝑎𝑡𝑡𝑒𝑟𝑛𝑠


RQ1: Comparison with PSO, SA, and RS

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GP PSO SA RS

CategoryPrecision

(%)

Recall

(%)

Precision

(%)

Recall

(%)

Precision

(%)

Recall

(%)

Precision

(%)

Recall

(%)

Financial 88 85 79 78 75 75 42 45

Science 75 100 50 100 43 100 17 67

Search 85 85 79 85 63 77 26 46

Shipping 90 90 53 80 57 80 19 40

Travel 80 86 89 86 81 79 27 36

Weather 76 87 72 87 61 73 20 33

Average 85 87 76 82 70 76 30 42


– 31 independent runs

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all financial science search shipping travel weather

25

50

75

100

25

50

75

100

recall

precis

ion

GP PSO RS SA GP PSO RS SA GP PSO RS SA GP PSO RS SA GP PSO RS SA GP PSO RS SA GP PSO RS SA

RQ1: Comparison with PSO, SA, and RS


GP

Category Precision (%) Recall (%)

Financial 88 85

Science 75 100

Search 85 85

Shipping 90 90

Travel 80 86

Weather 76 87

Average 85 87

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RQ2: detection efficiency


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Ambiguousservice Chattyservice Dataservice Multiservice Nanoservice

All

Financial

Science

Search

Shipping

Travel

Weather

0 25 50 75 1000 25 50 75 1000 25 50 75 1000 25 50 75 1000 25 50 75 100

type

precision

recall

RQ3: sensitivity towards antipattern types


Conclusion & Future Work

• We proposed an SBSE approach to automatically detect Web

service antipatterns.– Infer detection rules from antipattern examples

– Genetic programming

• Empirical evaluation on 310 Web services from 6 categories– Promising detection results of 85% of precision and 87% of recall

• Future Works– Include other types of anti-patterns

– Extend the base of examples

– Consider dynamic Web service metrics

– Recommend refactoring solutions to fix the detected antipatterns20


Thank you for your attention

Questions? Discussions? Suggestions?

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