Identification of Distributed Features in

SOA

Anis Yousefi, PhD Candidate

Department of Computing and Software

McMaster University

July 30, 2010

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Feature Identification

UserSoftware Engineer

List of classes

Feature being used

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Legacy Software Engineering!

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Feature Identification Literature• Identifying the source code constructs

activated when exercising a feature– Feature: functionality offered by a system

• Techniques – Textual analysis– Static analysis – Dynamic analysis– Hybrid approaches

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Trace-based Dynamic Analysis• Instrumentation– TPTP

• Scenario execution

• Trace analysis– Pattern mining

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Challenges

• Trace collection– Scattered implementation of features– Concurrency of events

• Feature location– Non-deterministic behavior of features

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Summary

• What? – Identify the code associated with distributed features in SOA

– Identify dynamic feature behavior

• How? – Trace-based dynamic analysis of services in SOA

– Pattern mining to identify feature behavior

• Challenges?– Scattered implementation of features, concurrency of events, non-

deterministic behavior of features

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Steps of The Proposed Approach• Run feature-oriented scenarios in SOA• Collect and merge execution traces of

services• Mine traces to extract patterns• Analyze the patterns to identify feature-

specific code and behavior

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12

3 4

The Proposed Framework

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10

Step 1- Running Scenarios

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`̀`

`

`

`

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m1 m2m3 m4…

Step 2- Merging Distributed Traces• Trace Structure

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Enter m1, Timestamp: 0Enter m2, Timestamp: 1Leave m2, Timestamp: 3Enter m3, Timestamp: 4Enter m4, Timestamp: 5Leave m4, Timestamp: 6Leave m3, Timestamp: 7…

Step 2- Merging(Contd.)• Problems

– Distributed data– Interweaved data

• Solution– Building “block

execution tree”– Resolving

uncertainties• Before-after

analysis• Textual analysis• Frequency analysis

– Merging the traces

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Step 3- Mining Frequent Patterns• Traces represent “call graphs”

• Mining frequent sub-graphs

Trace i Trace j

m0

m1

m2

m3

m4

m5

m6

m7

m0 m1m2 m3

m4m5m6m7

m0

m6

m2 m3m4

m5m6m7

m1

m0

m1

m2

m3

m4

m5

m6

m7

m6

X XY Y

Z W

Patterns: m1, Y

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m1, {i}m5, {i}m6, {i}m7, {i}X, {i}m4, {i}Y, {i}Z, {i}

m1, {i,j}m5, {i,j}m6, {i,j}m7, {i,j}X, {i,j}m4, {i,j}Y, {i,j}Z{i}m6, {j}W,{j}

Step 4- Analyzing Patterns

• Distinguishing “feature-specific patterns” from “omnipresent patterns” and “noise patterns”

Pattern 1 Pattern 2 Pattern 3 Pattern 4

Trace 1 * * - *

Trace 2 * * - -

Trace 3 * - * -

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Metrics: Feature Distribution

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,

Sn

50%

100% 25%

FD (f) = 1 - 58.3% = 41.7%

f

f: featurep: patternFD(f,p): distribution of feature f over SOA with regard to pattern pSp: services contributing in pattern pMs: methods defined in services sMp: methods contributing in pattern p

€

FD( f , p) =1−1

Sp∗

Ms ∩ M p

Mss∈S p

∑

€

FD( f ) =1−1

S f∗

Ms ∩ M f

Mss∈S f

∑

€

S f = Spp∈P f

U

FD(f): distribution of feature f (all patterns)Sf: services contributing in the execution of feature fMf: methods contributing in the execution of feature fPf: patterns for feature f

€

M f = M pp∈P f

U,

Sm

Sk

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mi

Metrics: Call Frequency

f

2 10 CF (f) = 12

€

CF( f , p) = number of calls to s. opop∈OPs∑

s∈Sp∑

€

CF( f ) =

CF( f , p)p∈P f

∑

Pf

f: featurep: patternCF (f,p): call frequency of feature f with regard to pattern pSp: services contributing in pattern pOPs: interface operations defined in services s

CF (f): call frequency of feature f (all patterns)Pf: patterns for feature f

OPi

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mi

Sn

Sm

Sk

Metrics: Accuracy

€

Acc( f ) =Pf

C f

Acc(f): accuracy of service regarding feature fPf: patterns for feature fCf: cases defined on the scenarios

=1 works as we expected>1 considers additional cases<1 treats some cases equally

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Future Work

• Abstract feature behavior– Normal vs. alternative behavior– Augment published service description

• Improve/Define metrics

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Thank You!

Anis Yousefi

yousea2@mcmaster.ca