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, Japan
2University 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
6

Typical Web service antipatterns
Multi-service Nano-service
7
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
8

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
9
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 2
category 1
category 3
Anti-pattern
instance

GP adaptation
NST
NOPT
COH
NOD
[1..200]
[1..100]
Quality metrics
NCT
Solution representation
11
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
12
𝑓𝑖𝑡𝑛𝑒𝑠𝑠 =
𝑖=1
𝑝
𝑎𝑖
𝑡
+ 𝑖=1
𝑝
𝑎𝑖
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?
13

Evaluation
• Studied Web service
– Benchmark of 310 Web services
14
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
15
𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠
𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑛𝑡𝑖𝑝𝑎𝑡𝑡𝑒𝑟𝑛𝑠
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝑎𝑛𝑡𝑖𝑝𝑎𝑡𝑡𝑒𝑟𝑛𝑠

RQ1: Comparison with PSO, SA, and RS
16
GP PSO SA RS
Category
Precision
(%)
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
17
all financial science search shipping travel weather
25
50
75
100
25
50
75
100
recallprecision
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
18
RQ2: detection efficiency

19
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 antipatterns
20

Thank you for your attention
Questions? Discussions? Suggestions?
21

Web Service Antipatterns Detection Using Genetic Programming

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