Towards Defining and Exploiting Similarities in Web Application Use Cases through User Session Analy...
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Towards Defining and Exploiting Similarities in Web Application Use Cases through User Session Analysis. Sreedevi Sampath, University of Delaware Amie Souter, Drexel University Lori Pollock, University of Delaware. Workshop on Dynamic Analysis (WODA), May 25, 2004 co-located with

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Sreedevi sampath university of delaware amie souter drexel university

Towards Defining and Exploiting Similarities in Web Application Use Cases through User Session Analysis

Sreedevi Sampath, University of Delaware

Amie Souter, Drexel University

Lori Pollock, University of Delaware

Workshop on Dynamic Analysis (WODA), May 25, 2004

co-located with

International Conference on Software Engineering (ICSE 2004)


Motivation and overview

Test case generation

Monitoring

load of

traffic

Content

personalization

Motivation and Overview

  • 12.3.40.65 GET index.jsp

  • 12.3.40.65 GET login.jsp

  • 12.3.40.65 GET reg.jsp? login=xxx&password=hello&

  • 12.3.40.65 GET myinfo.jsp

Behaviorally related

sequence of events

performed by the user

through interaction

with the system

View user sessions as use cases

Learn about dynamic behavior

User session analysis

  • Clustering via concept analysis

  • Common subsequences analysis

Software

development/

maintenance

tools

Test suite

reduction


Step 1 clustering via concept analysis

Step 1Clustering via Concept Analysis

  • Mathematical technique for clustering objects that have common discrete attributes

  • Set of objects, O: user sessions, us

  • Set of attributes, A: URLs, u

  • Relation, R: usrequestsu

  • Concept analysis identifies all concepts (Oi, Aj) for a given tuple (O, A, R)


Sreedevi sampath university of delaware amie souter drexel university

GD GR GL

({us1, us2, us3, us4, us5, us6}

,{GD,GR,GL})

({us2, us3, us4, us6}

,{GD,GR,GL,GS})

,{GD,GR,GL,PL,GS})

({us4}

({us3}

,{GD,GR,GL,GS,GB})

({us2}

,{GD,GR,GL,GS,GM})

({us6}

,{GD,GR,GL,PL,GS,GB})

CONCEPT LATTICE: FULL

(null,

{GD,GR,GL,PL,GS,GB,GM})

attributes (URLs)

RELATION TABLE

us1 us5

o

b

j

e

c

t

s

GS

x

x

x

x

x

us2

GB

PL

GM

us4

us3

us2

us6

SPARSE


Step 2 heuristic for test suite reduction

Step 2Heuristic for Test Suite Reduction

  • Smallest set of user sessions

  • Covers all the URLs

  • Represents common URL subsequences of different use cases

GD GR GL

us1 us5

GS

Identify next-to-bottom nodes

GB

Pick one user session from each of these

next-to-bottom nodes

PL

GM

us4

us3

Resulting reduced test suite:

{us2, us6}

us2

us6


Hypothesis motivating the approach

Hypothesis Motivating the Approach

  • Common Subsequences Hypothesis: The set of user sessions clustered together into the same concept node will have a high commonality in the subsequences of URLs in their sessions


Finding common subsequences of urls

Finding Common Subsequences of URLs

Subsequences of URLs are

representative of partial

use cases of the user sessions

us3

us6

Common

Subsequences

GD

GD

GR

GR

[GD, GR, GL]

[PL, GS]

GL

GL

NODE 003

[GR, GL]

PL

GB

objects

GS

PL

{ us3, us6 }

PL

GS

attributes

GR

GR

{ GD, GL, GR, GS, PL }

GL

GL

PL

GB

GS

PL

GS


Metric for common subsequences hypothesis

attr-size[5]: level 5

# user

sessions= 4

# user

sessions = 3

# URLs = 5

# URLs = 5

us2

us1

us3

{a, b, c, d, e}

a

b

c

d

a

b

e

a

a

b

c

e

a

b

e

d

a

b

c

d

a

b

e

Metric for Common Subsequences Hypothesis

  • attr-size[n] set: level of node in lattice

Metric

Percent of attributescovered

by common subsequences

of URLs of various sizes


Experiment applications used

Experiment: Applications Used

  • Bookstore web application

    • 9,748 LOC, 385 methods, 11 classes

    • Front end: JSP, Backend: MySql

    • 123 user sessions

  • uPortal application

    • 38,589 LOC, 4233 methods, 508 classes

    • Java, JSP, XML, J2EE

    • 2083 user sessions


Results for common subsequences hypothesis

Results for Common Subsequences Hypothesis

Bookstore


Results for common subsequence hypothesis

Results for Common Subsequence Hypothesis

Bookstore

Result: subsequences of various sizes cover reasonable percent of attributes


Conclusions for common subsequences hypothesis

Conclusions for Common Subsequences Hypothesis

  • Between user sessions of a node there exists commonality in subsequences of URLs

  • These common subsequences cover a reasonable percent of URLs (attributes) of the node

  • Clustering based on single URLs

    • clusters similar use cases

    • can choose one object from each node


Next to bottom coverage of use cases hypothesis

Next-to-bottom Coverage of Use Cases Hypothesis

In addition to covering all the URLs of the original test suite,

the user sessions in next-to-bottom nodes execute a

high percentage of the subsequences of URLs of the rest

of the original test suite

GD GR GL

reduced set

{us2, us6}

remaining set

{us1, us3, us4

us5}

us1 us5

GS

GB

PL

user sessions

belonging

to next-to-bottom

nodes

all other user sessions

except sessions

belonging to

next-to-bottom nodes

GM

us4

us3

us2

us6


Results for next to bottom coverage of use cases hypothesis

Results for Next-to-bottom Coverage of Use Cases Hypothesis

Metric: loss of coverage of use cases in remaining set by the reduced set

Bookstore

Result: short sequences present but long sequences are missing


Conclusion for next to bottom coverage of use cases hypothesis

Conclusion for Next-to-bottom Coverage of Use Cases Hypothesis

  • Long sequences absent but smaller sequences are present in reduced set

  • reduced set contains more URLs hence may contain other URL sequences absent in remaining set

  • Moderately supports pickingnext-to-bottomnodes for reduced test suite


Pros and cons of our approach

Pros and Cons of Our Approach

+ Results from common subsequences hypothesis support using concept analysis for clustering user sessions

+ Experiments show little coverage loss (tech report) by reduced test suite

  • Results from next-to-bottom coverage of use cases hypothesis indicate further work needed on heuristic


Future work

Future Work

  • Explore additional heuristics

  • Additional user session analysis

    • Useful for other software engineering tasks


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