RESEARCH ON DISTRIBUTED SOFTWARE TESTING PLATFORM BASED ON CLOUD RESOURCE

International Journal of Computer Science & Engineering Survey (IJCSES) Vol.4, No.2, April 2013
DOI : 10.5121/ijcses.2013.4202 17
RESEARCH ON DISTRIBUTED SOFTWARE TESTING
PLATFORM BASED ON CLOUD RESOURCE
Shi Hengliang1
, Zhao Changwei1
, Huang Tao1
and Dong Yongsheng1
1
Computer School of Henan University of Science & Technology
shi_hl@sina.com.cn
ABSTRACT
In order to solve the low efficiency problem of large-scale distributed software testing , CBDSTP(
Cloud-Based Distributed Software Testing Platform) is put forward.This platform can provide continous
integration and automation of testing for large software systems, which can make full use of resources on
the cloud clients, achieving testing result s in the real environment and reasonable allocating testing jobs,
to resolve the Web application software configuration test, compatibility test and distributed test problems,
to reduce costs, improve efficiency. Through making MySQL testing on this prototype system, the
verification is made for platform architecture and job allocation effectiveness.
KEYWORDS
software testing platform, distributed job scheduling,ACO
1. INTRODUCTION
Compared with traditional software products, the degree of current software products’
integration,modulization and complication is becoming higher and higher, which makes the
testing of large-scale distributed software as a research hotspot. There are two reasons: the first is
the development of Internet makes Web application system’s complexity and scale increasing.
The traditional centralized software testing methods have certain limitations, and the virtual
testing environment and data is difficult to be realistic. The second is that global software
development has become a trend, making software development is from centralized to the
distributed method to realize software continuous integration and test anywhere and anytime.
Aiming at the problems of software testing, domestic and foreign scholars have put forward a
series of test tools and frameworks. In accordance with the realization way,software testing
platform can be mainly divided into centralized testing and distributed test. Based on the basis of
local testing, centralized software testing framework usually is only for certain type or structure
of software testing, which is characterized as higher testing cost and lower efficiency. Li[1]
proposed a testing framework for model transformation, Stocks and Carrington[2] put forward a
testing framework based on specification introduction, Hartman proposed a model based testing
tools AGEDIS[3], Marinov proposed a new automation testing in Java program test framework,
realizing automatic generation of test cases and operation [4].
The distributed software testing framework, will test the different jobs assigned to the different
test termina. After the test is completed, the testing framework will collect intermediate test
results, and generate the final test report. Porter[5] proposed a distributed software testing

18
framework based on Skoll, making full use of Internet free client resources and establishment of
software automation test platform. In the industry, there are many companies who have
developed its test framework, such as Cloud Testing, Kite and SOASTA[6], but these frames can
only be used for the Web application performance testing and load testing. Zhu[7] proposed a
distributed environment for real-time software automated regression testing framework, this
framework can complete automatic regression testing according to the specific needs of dynamic
configuration system information.
In view of the existing software testing tools and frameworks in solving large distributed software
testing exists insufficient, the author put forward cloud resources-based distributed software
testing platform of CBDSTP(Cloud-Based Distributed Software Testing Platform), which
provides a large software system continueous integration and test automation, making full use of
cloud resources on the client resources, and realized test job allocation according to resources
reasonably, achieved software testing objectives in the real environment , resolved the Web
application software configuration test, compatibility test and distributed test question, which
reduces the test cost, and improves the test efficiency.
2. CONSTRUCTION OF CBDSTP
Figure1.CBDSTP Deployment Graph
CBDSTP ( Cloud-Based Distributed Software Testing Platform ) is based on cloud computing
resources ( computing resources, storage resources, cyber source ), which testing personnel
submit test job through the Web browser, and then platform receives the test job, generates test
case, and then assigns the testing job to different test job running clients, finally collects different
test records and intermediate results for test result analysis and test report generation. Job running
client is a cloud platform terminal volunteers, they can apply to join and leave testing platform
according to its own terminal performance, and obtain certain economic benefits on the basis of
their working hours. During the test job execution, the server will manage and monitor all clients
running testing job dynamically. As shown in Figure 1.

19
The system structure of CBDSTP is shown as Figure2. The platform consists of testing client, job
running client and servers. The structure between server and testing client takes B/S, and the
structure between server and job runniing client takes C/S architecture. Test staff client provide
the test tasks, test process and test results view view interactive pages. Job running client is
responsible for excetuing testing jobs and uploading test ing intermediate result. The server is the
core of the test platform, consisting by Web server, test server and database server component,
responsible for the reception and management of test job, generation of test subtasks and
scheduling of jobs, running the client dynamic monitoring, collection and analysis of test results,
adjustment of testiing plan.
Figure 2. CBDSTP System Architecture Graph
Web server in servers section provides testing personnel 4 user serive interfaces such as
registration/login, submitting testing tasks, querying testing results and registration of job running
client. Database server is for storage static and dynamic data of system which is the resource
sharing platform. Testing server consists of test task partition, test task scheduler, test results
manager and a test task state tracker. Test task scheduler can implement task allocation fully
based on dynamic parameters of running client resource. Test results management analysis test
results uploading from operation end, which will produce test report by analysis of testing results
through using software’s statistical measure criterion. State tracker is responsible for real-time
tracking task running end state to call exception handling module according to state information
correspondingly.
2. CBDSTP PROCESSING FLOW
Testing personnel login and upload test task. Server invokes the task partition to generate
subtasks. Task running client registrates in and installs client software, sends testing task
request according to their own needs. Server task assigns test task for running client. Task
running client establishes testing environment, runs test task and upload test results. Server
analysis and collects the intermediate testing results to generate the final testing report. Testing
personnel views the test results report through browser.

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Figure 3. CBDSTP processing flow
3. TESTING TASK PARTITION AND ALLOCATION ON CBDSTP
As a random search optimization algorithm, ACO algorithm and other simulated evolutionary
algorithm have defects, whose positive feedback makes the search results are prone to fall into
local optimal prematurally, i.e. when the search going into a certain extent, the solution of all
individual ants are basically as the same, but has been unable to obtain the optimal solution
stagnation from bigger range, that is to say it is unable to find the global optimal solution, it may
appear unbalanced phenomenon of load. Node resources overload easily result in cloud
computing task scheduling bottlenecks, which effects of task minimum completion time; node
resources under-load, the resources can not be fully utilized [8,9]. In swarm intelligence
optimization algorithms, there are PSO (Particle Swarm Optimization) algorithm[10], but PSO
algorithm is is difficult to understand,comparing with ACO algorithm, meanwhile the algorithm
is needed to encode for a large number of particles, so it requires larger work for the resource
node and task particles[11,12].
A typical job scheduling problem is described as the following: n jobs need to bedispatched onto
n node machines, and one node machine only processes one job, and one job only can be
implemented by one node machine[13]. So different dispatch plans have different execution cost
and resource consumption, job scheduling is to find one plan to make jobs completing smoothly
to ensure availability, reliability and optimality[14]. How to make a allocation to complete all
tasks with minimal cost is conversed into how to obtain task assignment matrix Rn*n. [15]
Suppose there are n ants to complete all the submitted jobs. Ant’s one trip stands for one job
dispatching procedure in which ant needs n walks which demonstrates dispatching one job,
tagging the walks as s; when all the ants complete one trip, can be thought as one loop finished;
Nc stands for the times of loop. And introduce two sets ask={task1,task2……,taskn} and
Node={node1,…,noden};Task stands for job set that is to be dispatched; and Node stands for
slave node machine set that is to be given jobs.
The traditional ACO algorithm was used in local pheromone renewal, the releasing
pheromones Q everytime is a constant. If the pheromone right of arc ( x, y ) is greater, the
number of ant who passes through the arc are more, so updating times of local pheromone is
more and more, which eventually leads to the information amount gap between different

21
arcs is too big, resulting in the algorithm searches trapped into local optimal. Therefore,
along with the algorithm search status changes, Q value should be constantly revised, and
not be a constant.
Definition 3.1 Suppose the number of ant passing by node X is m; and the number of ant
passing through arc ( x, y ) is M, then M / m is ant pheromone weight of arc ( x, y ).
Correction principle: Q(t)=Q*(1-(M／m)),and if m=0, Q(t) =0.
3.1 Global pheromone updating
During the Kth ant in the Q iteration, supposed there are a total of Rk ants passed by node X, and
there are rk ants who chose arc ( x, y ) as their following path. Algorithm for global update rule is
as the followging:
jijiij
old
iji
new
ij  ,,;*)1( 
(3-1)


m
k
k
ijij 1

(3-2)



 

else
jisfromkant
R
Q
k
k
k
ij
,0
ito)(),1(*

(3-3)
3.2 Local pheromone updating
In order to make the load more evenly, volatile balance factor is added in local pheromone
renewal rule. The balance factor is mainly used to describe the pheromone volatilization changes,
but is no longer constant.
Ordered:
,
max_
_
length
thislength
(3-4)
length_this represents the scheduling length, length_max for the iterative scheduling length this
job scheduling.
)1())((*)*1()1(  ttt ijijij 
(3-5)
3.3 Pheromone quantity control
In order to avoid algorithm stagnation, the quantity of pheromone on each path is limited in a
certain range[ min , max ], the value beyond of the range will be limited to min or max which
can be used to avoid the pheromone on a path is much larger than or is far less than the
pheromone on any other rest paths, causing all ants concentrate on the same path or away from a
path. And the setting of min or max is crucial, because each iteration path increases the

22
maximum information for 1/ )( gb
SL , which corresponds to a global optimal solution to this path
length, so when updating the optimal solution, it needs to update min or max simultaneously ,
and the pheromone min or max is inversely propotional to  and )( gb
SL , with proportional to
the number of excellent ant. The following rules are used to update dynamically.
))(*)1(2(
1)(max gb
SL
t




+ )( gb
SL

(3-6)
20
)(
)( max
min
t
t
 
(3-7)
Where is specified as the number of excellent ants.
4. IMPLEMENTATION AND VERIFICATION OF CBDSTP
CBDSTP is a software testing platform based on Web, using MyEclipse as development
environment, with Java as the development of language. System server is running in Linux
environment, and testing personnel client and test task running client run on Linux or Windows
OS. The system adopts MySQL as background database, for storing system dynamic information,
such as the test task information, test task running client information.
In order to verify the feasibility, experiments select MySQL, DTK2.0(EN) and DTP2.0(EN) as
measured software, included with a test script as test data. The goal is to validate the feasibility of
the platform architecture, so the simulation of some processing part ignores some problems that
should be considered in the actual environment, such as confidentiality and security.
Figure4. Tested task assignment graph
Figure4 shows the tested task assignment graph according to the ACO algorithm. We can see that
mysql7.0 is dispatched onto P1,P2,P3, and P4 physical machine. Respectively DTK2.0(EN) and
DTP2.0(EN) are also dispatched to some machines according to the burden of tested task.
The experimental conditions are as follows: 2 Linux servers connected by SSH seamlessly. 5 PC
with Windows and Linux OS of different configurations for running the testing task respectively;
3 ordinary PCs for testing personnel to use.
Testing personnel client: testers upload test tasks via the test task submission interface, including
testing task name, testing software code, test data, testing task running environment information,
test case list information. The test data contains test script and test cases. Testing task running
environment information includes testing task configuration information (such as the operating
system type, version), required testing tools and tool installation script. Testing case table
contains all cases name in tasks, the estimated execution time.

23
From Figure5, we can see the week average output or daily average output,and team output level.
Through the defects we can even trace back to the source problem.At the same time,we also can
grasp the team’s key tasks before distribution, and the task complete state in last period. we can
not only master the final results, but also the implementation process, even as the qulity and
quantity of tested softwares during certain period.
Figure5. Tested software monitor graph
Figure6. Intermediate Testing Result
Figure7.Final Testing Result

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The intermediate testing result and the final testing results are shown as Figure 6 and Figure 7
respectively. According to the experimental results, the tester is only required to submit the test
task, system can automatically implement sub-task partitioning, generating test subtask,
allocating sub-task, collecting and analysis test results,and shape the final test report.
5. CONCLUSION
Based on the idea of making full use of cloud resources, this paper presents a distributed software
test platform,which supports contineous online testing for large-scale software systems. And it
can achieve test task division and scheduling, collection and analysis and other functions which
provides a well solution for distributed software test. And many experiments verificate the
feasibility and reliability of prototype design and architecture.
ACKNOWLEDGEMENTS
Thanks for the Cloud_Test Group of Henan University of Science & Technology!It is their
deligent work that made this paper complete.
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Authors
Shi Hengliang, Ph. D, associate processor, research intrest includs cloud computing,
distributedcoumputing, software testing method and engineering.

RESEARCH ON DISTRIBUTED SOFTWARE TESTING PLATFORM BASED ON CLOUD RESOURCE

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