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, 2004co-located with

International Conference on Software Engineering (ICSE 2004)

Motivation and Overview12.3.40.65 GET index.jsp12.3.40.65 GET login.jsp12.3.40.65 GET reg.jsp? login=xxx&password=hello&12.3.40.65 GET myinfo.jsp

View user sessions as use casesBehaviorally relatedsequence of eventsperformed by the user through interaction with the system

User session analysis Test case generation

Monitoringload of traffic

Contentpersonalization

Test suitereduction

Softwaredevelopment/maintenancetools

• Clustering via concept analysis• Common subsequences analysis

Learn about dynamic behavior

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: us requests u

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

GD GR GL PL GS GB GM

us1 x x x

us3 x x x x x

us4 x x x x x

us5 x x x

us6 x x x x x x

us2 x x x x x

,{GD,GR,GL})({us1, us2, us3, us4, us5, us6}

,{GD,GR,GL,GS})

,{GD,GR,GL,PL,GS}) ,{GD,GR,GL,GS,GB}) ({us3} ({us4}

({us2} ,{GD,GR,GL,GS,GM})({us6}

({us2, us3, us4, us6}

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

{GD,GR,GL,PL,GS,GB,GM})(null, CONCEPT LATTICE: FULL

objects

attributes (URLs)RELATION TABLE GD GR GL

us1 us5GS

PL GB

us3

us2

GM

us6

SPARSE

us4

Step 2Heuristic for Test Suite Reduction

• Smallest set of user sessions• Covers all the URLs• Represents common URL subsequences

of different use casesus1 us5

GS

PL GB

us3 us4

us2

GM

us6

GD GR GL

Resulting reduced test suite: {us2, us6}

Identify next-to-bottom nodes

Pick one user session from each of these next-to-bottom nodes

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

NODE 003

objects

{ us3, us6 }

attributes

{ GD, GL, GR, GS, PL }

us3

GD

GR

GL

PL

GS

PL

GR

GL

PL

GS

us6

GD

GR

GL

GB

PL

GS

GR

GL

GB

PL

GS

Common Subsequences

[GD, GR, GL]

[PL, GS]

[GR, GL]

Subsequences of URLs arerepresentative of partial use cases of the user sessions

Metric for Common Subsequences Hypothesis

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

# user sessions= 4

# URLs = 5

# usersessions = 3# URLs = 5

attr-size[5]: level 5

{a, b, c, d, e}

Subseqsize

Common subsequence

Percent

attrs covered

1 a, b, c, d, e 100 %

2 ab, bc, be 80%

3 abc, abe 80%

us1 us2 us3

abcdabe

abceabed

abcdabea

MetricPercent of attributes covered

by common subsequences of URLs of various sizes

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

Bookstore

14

710

1316

1922

2528

3134

3740

Attr-Size Set

8

Subsequence Size

242628303234363840

42

44

46

48

50

52

Percent of Attributes Covered

Results for Common Subsequence Hypothesis

Bookstore

Result: subsequences of various sizes cover reasonable percent of attributes

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

reduced set{us2, us6}

remaining set{us1, us3, us4

us5}

user sessionsbelonging to next-to-bottomnodes

all other user sessions except sessionsbelonging to next-to-bottom nodes

GD GR GL

us1 us5GS

PL GB

us3 us4

us2

GM

us6

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

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

Bookstore

Result: short sequences present but long sequences are missing

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

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 picking next-to-bottom nodes for reduced test suite

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

• Explore additional heuristics

• Additional user session analysis Useful for other software engineering tasks