50120140501001

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME

TECHNOLOGY (IJCET)

IJCET

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 1, January (2014), pp. 01-10
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
www.jifactor.com

©IAEME

RELIABILITY IMPROVEMENT PREDICTIVE APPROACH TO
SOFTWARE TESTING USING MATHEMATICAL MODELING
D. Vivekananda Reddy1 and Dr. A. Ramamohan Reddy2
1

Assistant Professor Department of CSE, S.V.University, Tirupati
2
Professor Department of CSE, S.V.University, Tirupati

ABSTRACT
The main objective of any software testing is to improve software reliability. Many of
previous testing methods did not pay much attention towards how to improve software testing
strategy based on software reliability improvement. The reason to it as the relationship between
software testing and software reliability is a very complex task and this is because due to the
complexity of software products and development processes involved in it. However any Testing
strategy of software in order to improve reliability must need to possess the ability to predict that
reliability. For this purpose an approach is used called Model predictive control, which provides a
good framework to improve that predictive effect. T h e r e i s an n main issue in model predictive
control is that how to estimate the concern parameter. In this case, Empirical Bayesian method is
used to estimate the concern parameter: Reliability. This proposed reliability improvement predictive
approach to software testing using Empirical Bayesian method can optimize test allocation scheme on
line. In this the case study shows that it is not definitely true for a software testing method that can
find more defects than others can get higher reliability. And the case study also shows that the
proposed approach can get better result in the sense of improving reliability than random testing.
KEYWORDS: Software Testing,
Empirical Bayesian Method

Software

Reliability,

Model

Predictive

Control,

1. INTRODUCTION
Software testing is one of the most important methods to guarantee and improve software
Reliability. In the traditional opinion, the main aim of software testing is not to prove software is
Correct, but to detect software defects.

1

International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME
From this point, software test cases should be chosen for detecting more defects. However,
the Opinion that the main aim of software testing is to detect much more software defects is not
Universally reasonable. The reason that some researchers think the aim of software testing is to find
more defects are based on the assumption that fewer defects is consistent with higher reliability.
However, it seems it is not definitely true. The key problem here is software reliability is not only
Related to the defects distribution and is related to how to use the software as well. Frankl, P.G et al
discussed the relationship between so-called debug testing and operational testing and discussed how
to get delivered reliability. The purpose of debug testing is directed at finding as many bugs as
possible. On the other hand, the purpose of operational testing is to evaluate reliability under the
assumption that the software is subjected to the same statistical distribution of inputs that is expected
in operation. They pointed out debug testing may be more effective at finding bugs (provided the
intuitions that drive it are realistic), but if it uncovers many failures that occur with negligible rates
during actual operation, it will waste test and repair efforts without appreciably improving the
software. And then they pointed out operational testing, on the other hand, will naturally tend to
uncover earlier those failures that are most likely in actual operation, thus directing efforts at fixing
the most important bugs. However, sometime we cannot get good results only by operational testing.
We give a simple example to explain it. We assume that the input domain of a program is divided
into two subdomains with same size. Furthermore, we assume that one has 1000 failure-causing
inputs and another only has 10 failure-causing inputs, and actual users use the first input subdomain
with probability 51% and use the second input subdomain with probability 49%. If we assume that
all failures cause equal effects, a question arises: is it a good way to get higher reliability by using
operational testing? It seems it is not definitely true either. Therefore, neither debug testing nor
operational testing can guarantee to get higher reliability. In order to get higher reliability, we should
design software testing strategy with a clear quantified measure: software reliability.
However, it is very difficult for traditional software testing methods to do it. For most
traditional software testing methods, such as: random testing, partition testing, once the test case
selection schemes are determined, they will never be changed again at all. The question is: if we
cannot achieve the expected test goal in one testing step, what can we do? Of course, we will test the
software again and again until the test goal is achieved or some other stopping criteria are satisfied.
After we have tested the software some times, we should have some information about the software,
and then it should be better to use this information to design strategy of next testing step.
Model predictive control provides a good framework to improve predictive effect based on
the above philosophy. Model Predictive Control (MPC) is widely adopted in industry as an effective
means to deal with large multivariable constrained control problems. The main idea of MPC is to
choose the control action by repeatedly solving an optimal control problem on line. Therefore, it is
reasonable to put those software testing processes which are with some goals into the model
predictive control framework.
One of the main issues in MPC is the type of model of the system under control. In general,
the most utilized types of models are deterministic, stochastic, or fuzzy. Adaptive testing is a
software testing technique which results from the application of feedback and adaptive control
principles in software testing. It is a form of adaptive control or can be treated as the software testing
counterpart of adaptive control. In adaptive testing, the software testing proceed is divided into
many testing steps. As software testing proceeds, the understanding of the software under test and
the test suite is improved step by step. Some previous works reveal that adaptive testing can be used
to detect defects and can be used to assess software reliability. However, the models used in
adaptive testing are mostly to predict the detect defects or to assess reliability without defects
removed rather than to predict reliability improvement.
This paper we proposed Empirical Bayesian method based software testing strategy in
criterion of reliability improvement. This paper is organized as follows. Section 2 presents the
2

background of the paper. In Section 3, a model predictive control based software testing framework
is introduced. In Section 4, the software testing with Bayesian method is discussed in detail. In
Section 5, a case study is given.
2. BACKGROUND
2.1 Operational Profile and Testing profile
Musa said „a profile is simply a set of disjoint (only one can occur at a time) alternatives
with the probability that each will occur‟. An operational profile simply consists of the set of all
operations that a system is designed to perform and their probabilities of occurrence. It provides a
quantitative characterization of how the system will be used in the field, making it an essential
ingredient of software reliability engineering. Technically speaking, one can think of an operational
profile as a generic random variable that mainly indicates the operations that will be performed.
On the other hand, testing profile is to describe how to test the software while operational
profile is to describe how to use the software.
2.2 Software Run
Many software reliability models are based on the assumption that software reliability
behavior can be measured in terms of calendar time, clock time or CPU execution time. Although
this assumption is appropriate for a wide scope of systems, there are many systems, which depart
from this assumption. For example, the reliability behavior of a bank transaction processing software
system should be measured in terms of how many transactions are successful, rather than of how
long the software system operates without failure.
Cai proposed the conception of “a run” to describe discrete time. A run is minimum
execution unit of software. Any software execution process can be divided into a series of runs.
3. SOFTWARE TESTING AS A MODEL PREDICTIVE PROBLEM
The general software testing process is to use some testing strategy to generate test cases,
and then to test the software by using these generated test cases. Sometimes these test cases are
generated totally in one time, and sometimes these test cases are generated one by one or one batch
by one batch. In the latter, once the test case (or one batch of test cases) is/are generated, the
software will be tested by using this test case or this batch of test cases, and then see what will
happen. The strategy of selecting next test case or next batch of test cases will be improved
according to the test results of the previous step.
Model predictive control [3] provides a good framework to describe this kind of process.
Software testing process can be put into model predictive control framework. In this paper, goals are
reliability improvement and other constraints, such as test cost. In the decision making process, we
determinate the optimal testing profiles based on reliability improvement predictive and select test
cases according the corresponding testing profiles. We choose Empirical Bayesian method as
predictive model in this paper. So, the software testing process we followed in this paper is shown in
Figure 1, where r means inputs, Z means test results, and A can be a variable which describe how to
test the software. For example, A=i means the ith test case is selected or the test case is sleeted from
the ith equivalence class.

3


Fig 1: Empirical Bayesian Method To Software Testing
4. MODEL PREDICTIVE CONTROL BASED SOFTWARE TESTING WITH EMPIRICAL
BAYESIAN METHOD
In Section 3, a model predictive control framework based software testing process with
Empirical Bayesian method was introduced. In this section, we will discuss the details.
4.1 Empirical Bayesian Method Based Decision Making
Let
(i = 1,2,…….n) denote the action in the ith run .
the jth subdomain at the ith run.
Let denote testing profile at ith run.
Let

=[

|

= pr [

],1 j

, represent the test case is Selected in

]

1 if a failure occurs at the ith run
= 0 if no failure occurs at the ith run
( j = 1 , 2,……….m) denote the failure rate of
denote the failure rate of

after the ( i-1) th run.

denote the software reliability after the total i runs.

=

4

at the beginning of the Testing.

4.2 The Algorithm of Model Predictive Control Based Software Testing with Empirical
Bayesian Method
Therefore, the proposed model predictive control based software testing with Bayesian
method (STEBM) is as follows:
(1) Given the prior distributions of

(j=1, 2,……..m), operational profile

(j = 1, 2,………m),

the decrement of failure rate
(j = 1,2…..m) , reliability Goal and the maximum test steps n.
(2) i = 1.
(3) Let
{
| according to the testing profile
,
will get its maximum Prediction
value}. Then choose control action
and we will get the test result

according to the testing Profile

To run the software,

.

(4) Calculate the posterior distributions of

, after the

(1

) are known. This is so

called the feedback step in MPC.
(5) Estimate the reliability

=

(1-

).

(6) If the reliability reaches the given reliability goal or if i
(7) Calculate the prior distributions of
Just are the posterior distributions of

n, go to (9); otherwise go to (7).

. If

= 0, the prior distributions of

. If

= 1, the prior distributions of

Can be calculated based on the formula

=

-

.

(8) i = i+1 , go to (3).
(9) End.
5. CASE STUDY
The subject software in this case study is known as the Space program. Rothermel et al. [18]
describe the program as follows: “Space consists of 9,564 lines of C code (6,218 executable), and
functions as an interpreter for an array definition language (ADL),. The program reads a file that
contains several ADL statements, and checks the contents of the file for adherence to the ADL
grammar and to specific consistency rules. If the ADL file is correct, Space outputs an array data file
containing a list of array elements, positions, and excitations; otherwise, the program outputs an error
message”. The purpose of this case study is to show the difference between using the software
testing with Empirical Bayesian method (STEBM) and random testing (RT) in the context of
reliability.
According the function of the program we discussed, the program’s input domain was
divided into four subdomains.
In this study we assume the prior parameters as follows:

α 11 = 1, β 11 = 1; α 12 = 1, β 12 = 1,
α 13 = 1, β 13 = 1;α 14 = 1, β 14 = 1.
5

We used two methods to test the software respectively: the software testing with Bayesian
method (STEBM), random testing (RT). We did experiments as follows:
Sept 1. We tested the software by the method STEBM. The failure-causing defects were removed
when detected. We denoted this defect-removed software as Software 1.
Sept 2. We tested the software by the method RT. The failure-causing defects were removed when
detected. We denoted this defect-removed software as Software 2.
Sept 3. We run the Software 1 according the real operational profile. The failure-causing defect was
not removed when it was detected. The reliability was estimated.
Sept 4. We run the Software 2 according the real operational profile. The failure-causing defect was
not removed when it was detected. The reliability was estimated.
5.1 Experiment 1
We used two methods to test the software respectively: the software testing with Empirical
Bayesian method (STBM) with the assumption that the operational profile is ( 0.202 0.315 0.165
0.318), random testing (RT) with testing profile (0.202 0.315 0.165 0.318). We run the program 1000
times. Figure 2 shows the comparison between the two methods about the time to detect defects.

Fig 2: STEBM vs. RT of Experiment 1
Table 1 shows the numbers of detected defects of the two methods and the final reliabilities.
Table. 1: Total Detected Defects and Reliability For Experiment 1
Testing

Detected
Reliability

Strategies
STEBM
RT

Defects
15
14

0.9970
0.9942

In this case, the operational profile is consistent with defects distribution and RT use a very
powerful testing profile (it is consistent with defects distribution). From the results of this
6

experiment, we find that STEBM can find more defects than RT. Further more, STEBM can get
higher reliability than RT.
5.2 Experiment 2
We use two methods to test the software respectively: the software testing with Empirical
Bayesian method (STEBM) with the assumption that the operational profile is (0.3850.1 0.415
0.1), random testing (RT) with testing profile (0.202 0.315 0.165 0.318). We run the program
1000 times. Figure 3 shows the comparison between the two methods about the time to detect
defects.

Fig 3: STEBM vs. RT of Experiment 2
Testing
Detected
Strategie
Reliability
Defects
s
STBM
15
0.9983
RT
15
0.9961
In this case, the operational profile is not consistent with defects distribution and RT is with
testing profile which is consistent with defects distribution. From the results of this experiment, we
find that STEBM can not find more defects. In fact, in the most time of whole testing process, RT
can find much more defects than STEBM. However, STEBM can get higher reliability than RT.
5.3 Experiment 3
We use two methods to test the software respectively: the software testing with
Empirical Bayesian method (STEBM) with the assumption that the operational profile is
(0.385 0.1 0.415 0.1), random testing (RT) with testing profile (0.385 0.1 0.415 0.1). We run
the program 1000 times. Figure 4 shows the how fast the two methods to find defects.

7


Fig. 4 STEBM vs. RT of Experiment 3
Testing

Detected

Strategies

Defects

STEBM

15

0.9979

RT

15

0.9956

Reliability

In this case, the operational profile is not consistent with defects distribution and RT has
testing profile which is not consistent with defects distribution. From the results of this experiment,
we find that STEBM can find as many defects as RT can and can get higher reliability than RT.

6. CONCLUSION
The aim of this paper is to develop a reliability improvement predictive approach to
software testing with Empirical Bayesian Method. The interesting result of this paper is that it is not
definitely true for a software testing method that can find more defects than others can get higher
reliability. Because the proposed software testing in the paper can combine software testing with
reliability prediction together and consider the operational profile as well, it really can get good
result in the sense of improving reliability.
The initial values of related parameters play a key role in software testing, but it is difficult
to get the certain values of them. Empirical Bayesian method overcomes these difficulties, but
requires the users to develop prior distributions. There exist several ways to do this, such as the
method based on expert opinion. The expert opinion includes the empirical experience of other
similar software, the knowledge of software testing, or other background information.

8

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9

Authors

D. Vivekananda Reddy, working as Assistant Professor in the Dept of
Computer science and Engineering, S V University college of Engineering,
Tirupati, per past seven years and doing research in the field of software testing .

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