International Review on Computers and Software (I.RE.CO.S.), Vol. 11, N. 10
ISSN 1828-6003
October 2016
An Efficient DAGbM-KSJS Algorithm for Agile Software Testing
V. Alamelu Mangayarkarasi1, M. V. Srinath2
Abstract – Agile software testing is a software testing practice that follows the principles of agile
software development. In this paper, an optimal Agile software testing is performed using Directed
Acyclic Graph-based Model (DAGbM). Initially, the suggested model pre-processes the test case
dataset, then by deploying the Dependency Assessment for Use Case (DAUC) algorithm, the
dependency between the uses of cases are determined.
The K-Shingling based Jaccard Similarity (KSJS) algorithm estimates the similarity among every
test cases and prioritizes the clustered test cases using Span Clustering based Prioritization (SCP)
algorithm. After prioritizing the clustered test cases, the use cases are prioritized based on
dependency.
Finally, the minimum distance value is exploited for prioritizing the individual test cases. The
performance of the suggested method is validated using parameters such as code average, failure
rate, prioritization time, and percentage of defects detected. The validation results prove that when
compared to the existing methods, the suggested method provides optimal results for all the
parameters. Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved.
Keywords: Agile Software Testing, Directed Acyclic Graph-Based Model (Dagbm), Dependency
Assessment for Use Case (DAUC) Algorithm, K-Shingling Based Jaccard Similarity
(KSJS) Algorithm, Span Clustering Based Prioritization (SCP) Algorithm
developers cooperate with each other, and testing is
initiated at the end of every logic release. Multiple
software development industries have started using the
agile methods for software development [2]. When
compared to the traditional software development
methods, the agile methods have the following
advantages:
Accelerate time to market;
Increase the quality;
Increase the productivity;
Enhance the Information Technology (IT);
Enhance the flexibility.
The existing techniques used for the Agile testing are
Model View Controller (MVC) approach, JIC and JIT
approach, DAGue framework, and CA-DAG model [3].
These techniques consider the methods through which
the test cases are generated for Agile testing but they do
not execute the test cases in a sequential manner. Thus,
to address this issue, a DAG based model is proposed.
Generally, the lack of time and resources in the
companies prevent their ability to perform the testing.
Thus, prioritizing the execution order of the test cases
can enhance the software testing efficiency. Test case
prioritization is the process of scheduling the order of test
case execution such that the higher priority test cases are
executed first. The merits of using the test case
prioritization are as follows [4]:
Limited resource utilization;
Minimal time consumption;
Increased fault detection rate;
Enhanced system reliability;
Nomenclature
UC
TC
,
c
Z
(, )
()
()
(
ℎ
,
)
Use case
Test case
Jaccard similarity between the test cases
Empty set
Integer
Hash set
Hash set for test case i
Hash set for test case j
Size of the hash set
Distance between test case and
Threshold value
Dependency of test case i
Prioritization of use cases
I.
Introduction
Software testing is defined as the process of verifying
and validating the correctness of the developed software
product. As software testing is the last phase performed
before delivering the product to the customer, it is
considered as the final opportunity for validating the
system functions [1]-[39]. The different types of software
testing are represented in Fig. 1 [1]. Agile testing [27],
[28] is a software testing method that provides a
continuous iteration of development and testing
throughout the software development life cycle. Some of
the important properties of the agile testing are
unstructured, faster implementation, testers, and
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved
DOI: 10.15866/irecos.v11i10.10426
915
V. Alamelu Mangayarkarasi, M. V. Srinath
Increased function test coverage.
provided faster fault detection and increased the
regression fault detection rate.
Huang, et al[7] suggested a historical record based
cost-cognizant test case prioritization technique for
performing the regression testing.
Experimental results proved that the suggested
technique enhanced the effectiveness of fault detection.
Further, it increased the test effectiveness during
testing. Hettiarachchi, et al [8] suggested an enhanced
risk-based test case prioritization approach with fuzzy
based expert system for determining the requirement
risks in test case prioritization. Experimental results
proved that the prioritized tests detected more faults than
the other control techniques. Nejad, et al [9] suggested a
model based testing method for prioritizing the test cases.
Experimental analysis showed that the local search
method with Genetic Algorithm (GA) ([29]-[34])
enhanced the efficiency of the test case prioritization
process. Reddy and Reddy [10] proposed an optimal
prioritization technique for enhancing the fault detection
rate in regression testing. When compared to the
randomly ordered test cases and prioritized test case, the
suggested technique increased the fault detection rates.
Eghbali and Tahvildari [11] suggested a novel
lexicographical ordering based test case prioritization
technique. The suggested technique efficiently addressed
the ties and also increased the fault detection rate. Hema
Srikanth [4] suggested the Prioritization of Requirements
for Test (PORT) method for providing a system level test
case prioritization. Experimental results proved that the
suggested technique enhanced the failure detection rate
and also estimated that the customer priority was the
most significant contributor. Mei, et al [12] suggested a
refinement-oriented level-exploration strategy and
multilevel coverage model for prioritizing the test cases.
Experimental analysis proved that the suggested
technique increased the fault detection rate, and also
maximized the cost effectiveness of the test case
prioritization.
Fig. 1. Types of software testing
Objectives
The key objectives of the proposed DAGbM are as
follows:
To determine the dependency between use cases
using DAUC algorithm.
To deploy the KSJS algorithm for determining the
similarity of test cases.
To implement the SCP algorithm for clustering the
prioritized test cases.
To prioritize the use cases based on dependency value
and to prioritize the test cases using minimum
distance value.
The rest of the paper is organized as follows. Section
II illustrates the existing techniques used for prioritizing
the test cases and clustering the test cases.
Section III provides a detailed description of the
proposed DAG based model for performing the Agile
software testing. Section IV discusses the performance
analysis of the suggested model and the paper is
concluded in Section V.
II.
II.2.
Agarwal [13] suggested a Fuzzy-C-Means (FCM)
([35]-[39]) clustering approach based test case
prioritization technique. The suggested technique
exploited the factors such as code coverage ratio,
complexity, fault detection ratio and failure ratio for the
prioritization process. Jastej Badwal and Himanshi
Raperia Agarwal [14] suggested a clustering based test
case prioritization for enhancing the efficiency of code
average and functionality. The Average Percentage Fault
Detection (APFD) was used for clustering the prioritized
and non-prioritized cases. Though the prioritized test
cases provided optimal results than the non-prioritized
test cases, it did not enhance the efficiency of the
clustering in time. Carlson, et al [15] suggested a
clustering approach for enhancing the performance of
test case prioritization. The suggested approach enhanced
the fault detection rate of the test cases.
Related Works
This section discusses the merits and demerits of the
existing techniques used for test case prioritization, and
test case clustering.
II.1.
Test Case Clustering
Test Case Prioritization
Arafeen and Do [5] analyzed the capability of
traditional code analysis in enhancing the performance of
test case prioritization techniques.
Experimental results proved that the requirementsbased clustering on integration with the traditional code
analysis enhanced the prioritization techniques. Marijan,
et al [6] suggested a test case prioritization technique
named ROCKET for enhancing the efficiency of
continuous regression testing. The suggested technique
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Computers and Software, Vol. 11, N. 10
916
V. Alamelu Mangayarkarasi, M. V. Srinath
Further, it minimized the number of faults that slipped
the testing. Zhao, et al [16] suggested a hybrid regression
test case prioritization technique integrated with
Bayesian Networks based Test Case Prioritization
(BNTCP) approach for analyzing the fault detection
capability. When compared to the existing Bayesian
Network based approach (BNTCP), Bayesian Networks
based approach with feedback (BNA) and code coverage
based clustering approach, the suggested approach was
optimal. Rogstad and Briand [17] analyzed the
importance of clustering in grouping the regression test
deviations. The suggested approach minimized the
efforts spent by the testers for analyzing the regression
test deviations. Further, it enhanced the level of
confidence. Chaurasia, et al [18] suggested a clustering
based test case prioritization technique for executing the
test cases in a prioritized order. The suggested technique
maximized the bug detection rate, and also identified the
critical bugs as early as possible. Pang, et al [19]
proposed a Hamming distance based K-Means clustering
algorithm for classifying the test cases into effective and
non-effective. Experimental results proved that the
suggested technique provided higher recall ratio and
higher accuracy percentage. Medhun [20] suggested a
clustering based test case prioritization technique for
enhancing the code coverage efficiency.
The APFD analyzed the clustering of prioritized and
non-prioritized test cases. Yamini Pathania [21] proposed
a DBK Means clustering algorithm based test case
prioritization technique for enhancing the efficiency and
fault detection rate of the prioritization process.
Aichernig and Lorber [22] proposed a Directed
Acyclic Graph (DAG) for model-based test case
generation. The suggested model addressed the issues
created by increased state space. Patnaik, et al [23]
suggested an open structure algorithm for prioritizing the
test cases in an automated manner. The test cases that
were dependent on using the graph coverage values were
provided higher priority. The advantages of the
suggested approach were minimal speed for the testing
process and increased fault detection rate.
From the analysis of the existing techniques, it is clear
that they do not provide an optimal execution sequence
for the test cases. Further, the fault percentage in the
testing process is higher. Thus, to address these issues, an
efficient DAG based model is suggested for performing
the software testing.
III. DAGbM-KSJSF or Agile
Software Testing
The overall flow of the suggested DAGbM-KSJS is
represented in Fig. 2. From the figure, it is clear that the
key processes involved in the suggested model are as
follows:
Test case pre-processing;
Use case dependency estimation;
Similarity estimation over the test cases;
Optimized test case execution.
III.1. Test Case Pre-Processing
The initial step involved in the suggested DAGbM –
KSJS is test case pre-processing. The suggested model
exploits a mobile dataset as input. A sample of the
dataset is represented as follows:
Sample of the input dataset
Check for background music and sound effects# ON/OFF
Sound and background music
Check for background music and sound effects# Receive
the call and check
Check for background music and sound effects# verify if
sound effects are in synchronization
User Interface # Check in Landscape/Portrait mode
User Interface #Check for animation, movement of
characters, graphics
User Interface #there should not be any clipping
Fig. 2. Overall flow of the proposed DAGbM-KSJS for Agile software testing
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Computers and Software, Vol. 11, N. 10
917
V. Alamelu Mangayarkarasi, M. V. Srinath
During the pre-processing step, the suggested model
provides the use case ID and Test case ID for all entries
in the dataset. An example of the pre-processing step is
given in Table I.
TABLE I
EXAMPLE FOR DATASET PRE-PROCESSING
U_ID
Use case
Test case
1
Check for background
ON/OFF sound and
music and sound effects background music
1
Check for background
Receive the call and check
music and sound effects
2
User Interface
Check in Landscape and
Portrait mode
2
User Interface
Check for animation,
movement of characters
and graphics
Algorithm II: KSJS algorithm
Input:
Let i=1,2,3… N be set of
Let j=1, 2, 3…N be set of
Output:
Similarity & distance Measure of Test Cases
Begin
Set K=2, 3…Sizeof (Strings in
), flag=0
For each x
For all
ℎ
= 1,2, …
TListadd( )
For each
J=i+1;
Compute Similarity ( , )
( , )Split( , )
H(i) Compute K(shing( ))
H(j) Compute K(shing( ))
Compare ( ( ) , ( )))
If (Similarity>0)
Increment flag
Continue until N=
Compute
⁄∑ ( ( ) + ( ))
=
(2)
Compute
=
1(
,
)
(3)
( , )
DAG (
)
(4)
( , )
End for
End
Test ID
TID_1
TID_2
TID_3
TID_4
III.2. Use Case Dependency Estimation
After pre-processing the dataset, the dependency
between the use cases is determined using DAUC
algorithm. The suggested algorithm consumes a set of
use case and a set of the test case as input. For every use
case in the dataset, the dependency between them is
estimated using Eq. (1). Based on the dependency values
of the use cases, the test cases are grouped as represented
in Table II.
TID
TID_1,
TID_2,
TID_3
TID_6,
TID_7,
TID_19
TID_29,
TID_30
TABLE II
GROUPING OF TEST CASES
UID
Use case
1
Check for background
music and sound effects
Dependency
0.2
2
User interface
0.08
8
Save settings
0.33
The proposed algorithm consumes the set of use cases
and set of the test cases as input.
At first, the algorithm initializes the Shingling size
and flag variable, then for every test case, the similarity
with the other test case is determined using Jaccard
similarity represented as follows:
The steps involved in the suggested algorithm are
illustrated below:
Algorithm I:DAUC algorithm
Set of Use cases (UC)
Set of Test cases (TC)
Let i=1, 2, 3…. N be no. of
Let j=1, 2, 3…N be the no. of
Where m=1, 2, 3… N of UC
Begin
For each (c
),
Compute Z=∑
Compute
( ) = 1⁄
Continue until i=N
End for
End
,
(1)
=
∩
∪
(5)
The estimated similarity values are split and initialized
to the hash set ( , ), then the resultant values obtained
from the application of K-shingling algorithm is
allocated to the hash sets such as H (i) and H (j). When
the similarity between the test cases is greater than zero,
the flag is incremented. The distance between two test
cases is determined using Eq. (3). When the number of
iteration reaches the use case size, a Directed Acyclic
Graph (DAG) is constructed as represented in Fig. 3.
III.4. Optimized Test Case Execution
An optimized test case execution is possible through
test case clustering and prioritization. Hence, after the
construction of DAG, the test cases are clustered and
prioritized using SCP algorithm. The distance estimated
by Eq. (2) and the use case dependency values are
provided as input to the algorithm. For every use case in
the dataset, the average distance between the test cases is
estimated and initialized as the threshold value.
III.3. Similarity Estimation over the Test Cases
After grouping the test cases using DAUC algorithm,
the similarity and distance for every test cases are
determined using KSJS algorithm. The overall steps
involved in the suggested KSJS algorithm are illustrated
below:
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Computers and Software, Vol. 11, N. 10
918
V. Alamelu Mangayarkarasi, M. V. Srinath
Code coverage;
Number of test cases Vs. Number of clusters;
Failure rate;
Prioritization time;
Percentage of defects detected.
IV.1. Code Coverage
The code average is based on the number of
statements that are traversed by a particular cluster. It is
estimated for every test case executed in a test suite.
The computation of the code coverage is based on the
following equation:
Fig. 3. Example for DAG representation
=
ℎ
If the distance of a test case is less than or equal to the
threshold ℎ , the corresponding test case is added to
cluster 1, else the test case is added to cluster 2. After
clustering the test cases, they are prioritized. Similarly, as
represented in Eq. (7), the use cases are prioritized based
on their dependency values. For every use case, the
overall distance is computed such that the test cases that
have minimum distance are prioritized thus resulting in
an optimized test case execution.
(
(
))
TABLE III
COMPARISON OF CODE COVERAGE
Category
Code coverage
Non-prioritized
0.61
Prioritized
0.69
a. Number of test cases Vs. Number of clusters
The comparison of the number of test cases executed
with respect to the number of clusters is validated for the
prioritized and non-prioritized approaches. From the
comparison results represented in Fig. 4, it is clear that
the prioritized approach executes more number of test
cases than the non-prioritized approaches.
(6)
If (dis ( < ℎ ) || dis (
= ℎ ))
Add ( ) c1 ( )
Else
Add ( ) c2 ( )
Continue until i=
Order (C1) dis (
)
Order (C2) dis (
)
Apply
(c1, c2)
End for
For each ( )List(TC)
Compute
ResultProduce (Pr(
End for
End
IV.
=
(8)
The comparison of code coverage for the prioritized
and non-prioritized cases is represented in Table III.
From the table, it is analyzed that the code coverage
for the prioritized cases is more than the code coverage
for the non-prioritized cases.
Algorithm III: Span Clustering based Prioritization
(SCP)
Inputs:
Computed Distance & Dependency values
Output:
Clustered & prioritized Test Cases
Begin
For each
ℎ
= 1,2, …
ℎ = Compute
× 100
b. Mean Average Percentage of Fault-Detection
(APFD)
The APFD estimates the rate of fault detection per
percentage of test suite execution.
It is estimated by considering a weighted average of
the percentage of faults detected during the test suite
execution. Generally, the values of the APFD varies from
0 to 100. Higher the value of APFD, better the fault
detection rates. The comparison of APFD for the existing
string-based technique with pre-processing, string based
technique without pre-processing [24] and the proposed
DAGbM algorithm is represented in Fig. 5.
Four test case datasets such as Ant V6, Derby V1,
Derby V2 and Derby V3 are used for the experimental
analysis. From the figure, it is analyzed that the
suggested DAGbM algorithm provides higher APFD
than the existing algorithms.
(7)
))
c. Prioritization time
The prioritization time is defined as the average time
consumed for performing the test case prioritization
process. The comparison of prioritization time for the
existing Greedy, ART, Genetic Algorithm (GA) [25]
Performance Analysis
This section analyzes the performance results of the
proposed DAGbM-KSJS model for the following
metrics:
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Computers and Software, Vol. 11, N. 10
919
V. Alamelu Mangayarkarasi, M. V. Srinath
algorithms and the proposed DAGbM is repres
represented
ented in
Fig. 6. From the figure, it is clear that the suggested
DAGbM algorithm consumes minimal prioritization time
than the existing algorithms.
NonNon-prioritzed
prioritzed
Prioritized
Number of test cases executed
90
80
70
60
50
40
30
20
10
0
and weight factor based prioritization algorithm [26
26].
The validation
validation result is represented in Fig. 7.. From the
figure, it is analyzed that when compared to the existing
algorithms, the suggested DAGbM algorithm detects
higher number of defects in tthe
he execution of the test
cases.
Random
100
% of defects detected
80
60
40
20
1
2
3
4
Number of clusters
5
0
10
Fig. 4. Comparison of number of test case
casess executed
Vs. number
number of clusters
String based technique with pre-processing
pre processing
String based technique without pre-processing
pre processing
DAGbM
DAGbM-KSJS
KSJS
V.
80
APFD (%)
40
20
0
Derby V1 Derby V2
Test case dataset
Derby V3
Fig. 55. Comparison of Mea
Meann APFD for the existing
and proposed algorithms
Greedy
ART
8
6
4
2
0
10
20
30
40
50
60 70
80
30 40 50 60 70 80
% of test cases executed
90 100
Conclusion and Future Work
In this paper, an efficient DAGbM
DAGbM-KSJS
KSJS algorithm is
proposed for performing the A
Agile
gile software
software testing. The
suggested algorithm initially pre
pre--processes
processes the test case
dataset for providing the test case ID and use case ID.
After pre
pre--processing
processing the dataset, the dependency
between the use cases is estimated using DAUC
algorithm. Based on the dependency values, the test
cases are grouped, then for every test case in the use
case the similarity and distance from other test cases are
case,
estimated using KSJS algorithm. With the estimated
distance values,
values, a Directed Acyclic Graph (DAG) is
cons ructed and the average distance between dependent
constructed
test cases are determined.
For every use case, the test cases that has minimal
distance are clustered.
clustered. The
The SC
SCP
P algorithm is executed for
prioritizing the clustered test cases. After prioritizing the
test cases, the use cases are prioritiz
prioritized
ed using the
dependency values.
From the overall distance value
valuess estimated,
estimated, the test
cases that has minimal distance are provided higher
priority for execution. To validate the performance of th
thee
suggested algorithm, the parameters such as code
coverage, number of test cases vs. number of clusters,
failure rate, prioritization time, and percentage of ddefects
efects
detected are considered.
Experimental results prove that the suggested
algorithm provides optimal results for all the metrics than
the existing algorithms. As a future enhancement, the
proposed DAGbM-KSJS
DAGbM KSJS algorithm can be deployed in
distributed environment for enhancing the efficiency of
agile
scalability
ity
agile software testing. Further, the speed and scalabil
of the agile software development can be improved.
60
Ant V6
20
Fig. 7.. Comparison of % of def
defected
ected detected for the existing
and the proposed methods
100
Prioritization time (seconds)
Weigt factor based prioritization
algorithm
DAGbM-KSJS
DAGbM KSJS
90 100
% of pools used
Fig. 6.. Comparison of prioritization time for the existing
and proposed algorithms
d. Percentage of defects detected
The effectiveness of the proposed DAGbM algorithm
is vali
dated against the existing random order execution
validated
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Compu
Computers
ters and Software, Vol. 11, N. 10
920
V. Alamelu Mangayarkarasi, M. V. Srinath
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Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Computers and Software, Vol. 11, N. 10
921
V. Alamelu Mangayarkarasi, M. V. Srinath
Fuzzy C-Means with Gabor Filter and Minkowski Distance,
(2015) International Review on Computers and Software
(IRECOS), 10 (10), pp. 1054-1061.
Authors’ information
1
Assistant Professor, Department of Master of Computer Applications,
STET Women’s College, Mannargudi, India.
2
Director, Department of Master of Computer Applications, STET
Women’s College, Mannargudi, India.
V. Alamelu Mangayarkarasi is an Assistant
professor of MCA, Sengamala Thayaar
Educational
trust
Women’s
college,
Sundarakkottai, Mannargudi. She has 8 years of
experience in teaching. Currently she is
pursuing Ph.D. in the Bharathidasan University,
Tiruchirappalli, India.
M. V. Srinath is director department of MCA,
Sengamala Thayaar Educational trust Women’s
college, Sundarakkottai, Mannargudi. He has
more than 16 years of experience in teaching
and Research. He completed Ph.D. in the year
2005 at NITTTR, university of Madras.
He published 30 research journals, attended 67
and more number of seminars, conferences and
workshops both national and international.
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Computers and Software, Vol. 11, N. 10
922
V.ALAMELU MANGAYARKARASI