A Review on Software Testing Approaches for Cloud Applications

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Perspectives in Science (2016) 8, 689—691

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A review on software testing approaches

ଝ

for cloud applications

∗

Tamanna Siddiqui, Riaz Ahmad

Department of Computer Science, Aligarh Muslim University, India

Received 20 February 2016; received in revised form 9 June 2016; accepted 9 June 2016

Available online 4 July 2016

KEYWORDS Summary Cloud computing has actually been invented to be the latest computing standard

that will work several distinctive research areas, such as software testing. Testing cloud applica-

Cloud testing;

Clustering; tions will keep its unique characteristics that involve more recent testing techniques. Software

Prioritization; testing helps to reduce the need for hardware and software services and also provide adaptable

Privacy; and valuable cloud platform. Testing within the cloud platform is easily manageable based on

Security; new test models and criteria. Prioritization approach is made responsive to build much better

relationship between test cases. These test cases are clustered dependent on priority level.

Test cases

The resources can be used properly by applying load balancing algorithm. Cloud guarantees

maximum usage of existing resources. But, security deﬁned as a primary problem in cloud. At

the present time, organizations are progressively moving excited about deploying and making

use of ready-prepared business applications, with particular short-term to the marketplace.

The possible lack of capital budgets for software planning and on principle deployments, along

with the swift progression of cloud these are the reasons why one should make the interest

on business application. However, these are the interests that help make the SaaS based busi-

ness application on-demand. In this paper different approaches has been discussed that will

help to extend the cloud environment. Also, the study of several well-known software testing

approaches.

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Introduction making use of virtual machines (VM) to do testing is not a

unique idea. The issue starts when those VM are unavailable

as needed. Some issues that faced like servers, data source

Software testing is used to ﬁnd out the functionality and

as well as applications not representing virtualized environ-

validity of a program over performance and analysis. By

ments. To address this challenge migrate the tests way to

the cloud. Cloud computing provides you with other norms

ଝ of testing agility which gives a paradigm shift that a pay-

This article belongs to the special issue on Engineering and Mate-

per-use technique but not creating in-house data canters. rial Sciences.

∗

Corresponding author. Tel.: +91 9412273378. The users have the ability improve their computing choices

E-mail address: [email protected] (R. Ahmad). to fulﬁll their requirements on-demand (Prathibhan et al.,

http://dx.doi.org/10.1016/j.pisc.2016.06.060

690 T. Siddiqui, R. Ahmad

Figure 1 Cloud computing testing techniques.

2014; Doss et al., 2012). It seen to apply virtualized hard- Clustering is a bottom-up clustering method each observa-

ware appropriately unrestricted storage also software vital tion starts in its own cluster (Liu et al., 2013). It utilized

for thin-client network right to use the infrastructure. By in several ﬁeld because of it offers ability to use arbitrary

putting on prioritization strategies simply to make the most clustering dissimilarity or distance function. Clusters are

of the scope and plan the test cases in a sequential order to design by a couple of objects determined by the squared-

get the code coverage at the quick rate. Clustering models error objective functions because highly responsive to initial

are useful to software providers to ﬁnd his services to the conditions when various size and non-globular state. Algo-

cloud users and helps the service expert to extend use of rithms design by using the part of the data in Partition-based

software service toward the cloud nature. Clustering. Once every cluster expands clustering elements

and minimizes the sum of squared distance from the mean

within every cluster. Moreover it used to minimize the clus-

Testing in cloud computing

tering criteria by iteratively moving the data between the

clusters till an ideal partition is seen.

The testing framework in cloud computing consists of four

most important techniques (Fig. 1).

Load distribution scenarios

Prioritization techniques

Load balancing aims to improve resource use, extend

throughput, eliminate response time and avoid overload

Prioritization techniques are used to improve the function of any single resource. Load balancing technique will

and give a direction to develop and run test cases. Test involve different levels of the cloud in load balancing deci-

cases priority assign by using graph coverage value. It is usu- sion. It mainly found in master slave form (Penmatsa and

ally deﬁned as the amount of the complexities that involved Chronopoulos, 2011). The static load balancing algorithms

dependent test cases which is determined by total number helpful to deﬁnes the job to the node according to the

of dependents and greatest path of overall dependents of potential of the node to process new queries. The process is

the test case. Weight factors to be used for prioritization calculated on previously ability to use the nodes properties,

strategies that are customer priority, requirement complex- working power, space for storing capacity and functions. In

ness and volatility (Anitha and Srinath, 2014). High factor dynamic includes several features of nodes functions and

values show need for prioritization of test case relating to network capacity. It is also combos of prior gathered details

the requirement. Test coverage analysis is simply an amount about the nodes in the cloud. According to the attribute

applied in software testing named code coverage analysis gathered, Algorithm assigns and reassigns the task to the

(Indumathi and Selvamani, 2015). It deals only source codes nodes dynamically depending on the prior gathered details

of program that used within the testing process. The cov- about the nodes in cloud area. In Centralized Load Balanc-

erage prioritization practice prioritizes the test cases in ing (CLB) technique, the assigning and arranging actions are

decreasing order and determined by the sum of ReqSlice produced by one node that chargeable for keeping the infor-

(number of requirements declared by test case) and ReqEx- mation based upon entire cloud set-up. It is usually highly

ercise (number of requirements utilized by test case). The overloaded and low fault tolerant (Berander and Andrews,

exact time is checked for measuring the expense for every 2005).

test case because test case prioritization is performed based

on cost examination in cost based prioritization (Mohanty

Security mechanism

et al., 2011).

Difﬁe—Hellman algorithm offered a method to sharing keys

Clustering techniques between sender and receiver for encryption and decryption

of information and data. It arranges most group members in

Clustering can be considered the key unsupervised learn- a logic ring to sharing DH public keys Salzbrunn et al. (2008);

ing situation. So, the other issues it focuses on ﬁnding a Liao and Chao (2008). The arbitrary parameters produce new

structure in an amount of unlabelled data. Agglomerative shared keys for every message that is exchanged between

Software testing approaches for cloud applications 691

Table 1 Software testing techniques in cloud platform.

Techniques Sub techniques Quality measurement

Test case prioritization techniques DSPT IDT, MFD, CS, RT

RBPT RC, CC, TCL

CBPT Weight in each row using UWD & WD

CEBPT IFT, TST, Whole test case cost

Clustering algorithm AC CT, half distance matrix computation

K-MC Dataset sizes, overhead at the server side

PBC Hedgehog representation of the ﬂow

Load balancing scenarios HLB Execution time and total performance

SLB Expected response time, fairness index, system utilization

DLB Power utilization expense, average response time, Usage rates

CLB Capability matrix, load matrix

Security mechanism DHA I.F., decryption, computation of point on the curve

DESA Proﬁtable rate under toleration distance

AES Timing simulation

each other. Wang’s approach relates to the security and References

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