J. Software Engineering & Applications, 2010, 3: 331-340
doi:10.4236/jsea.2010.34038 Published Online April 2010 (http://www.SciRP.org/journal/jsea)
331
Test Effort Estimation Using Neural Network
Chintala Abhishek*, Veginati Pavan Kumar, Harish Vitta, Praveen Ranjan Srivastava
Department of Computer Science and Information System, Birla Institute of Technology and Science, Pilani, India.
Received January 5th, 2010; revised February 21st, 2010; accepted Februa ry 25th, 2010.
ABSTRACT
In software industry the major problem encountered during project scheduling is in deciding what proportion of the
resources has allocated to the testing phase. In general it has been observed that about 40%-50% of the resources need
to be allocated to the testing phase. Ho wever it is very difficu lt to predict the exa ct amount of effort required to be allo -
cated to testing phase. As a result th e project planning goes haywire. The project which has not been tested sufficiently
can cause huge losses to the organization. This research paper focuses on finding a method which gives a measure of
the effort to be spent on the testing phase. This paper provides effort estimates during pre-coding and post-coding
phases using neural network to predict more accurately.
Keywords: Test Effort Estimation, Neural Network, Use Case Points, Halstead Model
1. Introduction
Software engineering [1] is a field that provides stan-
dardized approaches for the development, operation, and
maintenance of software. Software Engineering as a
discipline the need arose when there was software crisis
[1]. The need for producing software of high quality and
to have a control on the effort both in terms of the money
and the person-hours gave software Engineering a higher
prominence. It defines a process which helps for project
management [1].
A crucial aspect of software Engineering is software
testing [1]. Software testing is a phase of software
development which deals with testing the developed
product or project. A project /product which have been
developed without sufficient testing might contain major
bugs which can render the entire project useless and also
cause losses of critical data.
Software testing [1] by definition is the process of
validating and verifying a software product or a project
or an application. It should be tested on the aspects of:
meeting the requirements of the user, functionality, and
characteristics of the developed software.
Generally software test life cycle involves several
stages and it can be classified into three major phases.
Initial phase: This phase involves with identifying
which aspects of the designed are to be tested followed
by the creation of a test phase strategy.
Intermediate phase: This phase involves the deve-
lopment step in which the procedures and the scenarios
all are defined. This is followed by the execution step
which deals with implementing the developed plan and
reporting any error found.
Termination phase: This is longest phase involves
several activities, once the testing is finished a report
indicating the fitness of the pro ject/pro duct to be released
is created. Then the analysis is carried out with the client
to deal with the problems faced during its real time
implementation. Then the detection of any further exi-
sting defects is carried out. If there are any modifi-
cations done then the entire component is retested to
determine any side effects that could have occurred
because of changes in previous step (Regression testing).
If the system meets the exit criteria the testing phase is
terminated.
In the ideal scenario it is desirable to have exhaustive
testing as this ensures that there are no bugs or errors.
This is not possible even with a project of very less
complexity. Thus the need for having an efficient testing
strategy arises. Software testing phase needs to be
planned to be carried out efficiently.
Artificial neural network [2-4] is a soft computing
technique that tries to achieve the functionality of
biological neural network. It consists of group of
artificial neurons that work on mathematical model to
process the information and to solve highly complex
problems. It involves a network of simple processing
elements called as neurons that are connected. The
connections between the neurons help in realizing a
complex functionality. As mentioned above Software

Test Effort Estimation Using Neural Network 332
testing is a challenging field and this paper proposes and
efficient methodology to estimate test effort estimation
with more accuracy using artificial neural network.
The paper is written with the general introduction of
the Software testing in Introduction, followed by the
description about the background work (Section 2).
Section 3 deals with actual problem wh ile section four is
fully devoted on proposed approach of the paper. Section
4 deals with the application of the proposed model and
finally in Section 6, the results obtained are discussed.
2. Background Work
Estimation accuracy can be achieved by choosing an
accurate model for measuring. This section provides with
the information that has been gathered, on which the
work is based up o n.
2.1 Use Case Point [5]
The effort to be estimated for the pre-coding phase is
based on the use case point analysis [5]. Nageswaran [5]
proposes a strategy which calculates effort based on the
unadjusted use case weight (UUCW), unadjusted actor
weight (UAW) and the technical and environmental
factors (TEF). Those factors are calculated based on the
classification of actors and usecases into simple, average,
complex and very complex classes. The obtained
unadjusted use case point (AUCP) is multiplied by a
factor to obtain the effort. The effort obtained in this is
not accurate with the expected level of accuracy in
estimation. Our proposed model is totally inspired by
Nageswarn work. This paper provides an improvement
over the method proposed by Nageswaran [5]. Nages-
waran [5] model can be stated as:
During this phase the project manager has the design
document based on which he can make an estimate of the
effort that needs to be allocated to the testing phase.
The proposed method suggest the usage of adjusted
unadjusted usecase weight, unadjusted actor weight
(UCW), technical and environmental factor (TEF) as a
measure for the test effort estimation. Back propagation
in neural network is used for training the network.
The inputs are taken for a particular project based on
the design document. The UUCW is calculated as
Usecase component:
UUCW = (No. of usecases of type simple*1 + No. of
usecases type average*2 + No. of usecases of type
complex*3 + No. of usecases of type very complex*4)
The usecase information Table 1 is used for distin-
guishing and assigning the values.
Actor components:
The actor information is obtained from the Table 2.
TEF components:
The technical and environmental factors are assigned
as indicated by the Table 3.
Table 1. Usecase weight assignment table [5]
Usecase type Description Weight
Simple <=3 1
Average 4-7 2
Complex >7 3
Table 2. Actor weight assignment table [5]
Actor type Description Weight
Simple GUI 1
Average Interactive 2
Complex Low interaction 3
Table 3. TEF weight assignment factors [5]
Factor Description Assigned
value
F1 Test tools 5
F2 Documented inputs
5
F3 Development
environment 2
F4 Test environment 3
F5 Test ware reuse 3
F6 Distributed system 4
F7 Performance
objectives 2
F8 Security 4
F9 Complex interface 5
UAW and TEF are calculated as:
UAW =

Actor weight*number of actors
TEF =

Assigned Weig ht*assigned value
2.2 Halstead Model [6]
The background study involved studying Halstead model
[6]. A brief explanation of it is given here. It makes use
of some primitive measures to determine the length and
the volume of the program [6]. It makes use of the
factors such as total number of operators (n1), total
number of operands (n2), total number of their operator
occurrences (N1) and the total number of operand
occurrences (N2). He also proposes a formula for
measuring the development effort and development time
using such measures.
The length N is estimated according to Halstead as:
N = n
1log2 n1 + n2 lo g2 n2
The program volume is given by the formula
V = N*log2(n1+n2)
A volume ratio is defined by him, represented by L, its
value should not be more than one. It is represented by
the formula L = 2/n1*n2/N2
The effort is given by the formula
Effort = (( n1 * N2)/ (float (2 * n2)) * N *log( n, 2 )
This is the effort as estimated by the Halstead model

Test Effort Estimation Using Neural Network333
and is obtained in elementary mental discriminations.
2.3 Cognitive Complexity [7]
Kushwaha [7] suggests that effort can estimated based on
the total weighted information count of line of code and
software and basic control structures. This method
involves a complex estimation function. The effectiveness
of which has not been est abl i shed f or la r ge pr ojects.
2.4 Effort Estimation Using Soft Computing
Techniques [8]
Sandhu [8] shows that soft computing technique–neu-
ronfuzzy can be applied for effort estimation by esta-
blishing its accuracy by comparing it with various other
models. The estimation was done on NAS A pr o ject data.
Neurofuzzy was able to estimate the nonlinear func-
tion with more accuracy. This paper helps in suggesting
that effort estimation based on soft computing is indeed a
right direction of accurate estimation.
Introduction to neural network:
2.5 Neural Network [2-4]
The neural network structure is used for solving complex
problems [2-4]. The Backpropagation methodology is
used for training the neural network. A set of input
training data and the expected output is created and the
network is trained with the training set. The network is
trained over multiple iterations. Over the multiple
iterations the network tries to converge towards the
expected output and thus training itself with the required
training function. The trained network is provided with
the inputs from a test set and it gives the output which is
the estimated output.
2.6 Neural Network Structure [9]
The neural network structure is realized using the
freeware Neuroph neural network framework [9]. Easy-
Neurons [9] is the GUI application for it. It is a java
library. The multilayer perceptron model is used for
creating a neural network, as this would be the appro-
priate network structure which would help in realizing
the problem. In a multilayer network there will be one
input layer, atleast one hidden layer and one o utput layer.
Backpropagation is used as the training methodology i.e.,
the learning rule. It is a supervised learning algorithm. It
is a learning methodology through which the network
trains itself through multiple iterations over the test data.
It does so by reducing an error function. The network
eventually converges towards accurate values as it is
trained with more and more training data.
The activation function used h ere is th e Tanh function .
The activation function is an abstraction of the action
potential. It represen ts whether the cell should fire or not.
The Tanh function is normalized .It is real valued differ-
ential curve, as represented in the Figure 1.
hyperbolic tangent function
Figure 1. Tanh function
A brief description of the conventional pre and post
coding effort estimation models is given here:
2.7 Conventional Methods for Pre Coding Effort
Estimation [9]
1) The testing phase effort is not generally calculated.
Once the product is designed the rest of the resources in
terms of the budget and time are allocated to the testing
phase. This methodology can be applied for mission
critical system testing, as any compromise in the quality
of the product would lead to huge losses [1].
2) Another method which is used for planning the
testing phase effort is the percentage of the total
development effort to be spent on testing. This also
doesn’t provide with efficient planning of resources.
2.8 Conventional Methods for Post Coding
Effort Estimation [9]
1) Based on Software size:
The software size is available from the code and a
productivity figure is applied to it. It involves the
multiplication of number of function points and effort per
function point. This approach is too simplistic, it
involves estimations based on other project data which
can lead to errors and it includes rigorous data main-
tenance [1].
2) Delphi Technique:
This technique involves a group of experts answering
a questionnaire and arriving at a converging solution to
the problem. The technique is time and resource con-
suming and generally doesn’t lead to accurate predictions
[1].
3) Test case enumeration based estimation:
It involves the enumeration of the entire test cases and
the effort for each test case is estimated and beta
distribution is applied over it. It is time consuming
process.
3. Actual Problem
The test effort estimation is a big challenge in project

Test Effort Estimation Using Neural Network 334
actors involved in the system. planning. There are no models presently available that can
estimate the test effort accurately. The effort that needs to
be spent on the testing phase needs to calculate precisely.
The effort needs to be estimated both before the coding
phase and after the coding phase. A comparison of the
observed efforts should not be large, which i s an i ndi cati on
of effective model. The problem is to propose a model
which estimates the effort accurately. The proposed model
should not be dependent on the ty pe of projec t.
Usecase component: This takes information about the
usecase involved in the design document.
TEF component: This takes the information regarding
the technical and environmental factors involved in the
system.
Further description about these components is given
ahead in the paper.
The post coding effort estimation takes input from the
code document and it has three components. They are:
Variables component: This takes the information
regarding the variables involved in the system.
4. Proposed Approach
4.1 Architecture Complexity component: This takes the information
regarding the complexity of the system.
The architecture involves two components: pre and post
effort estimation components and learning rule used here
is Back propagation algorithm as shown in the Figure 2.
Criticalness component: This takes information reg-
arding the criticalness of the system.
These are further discussed ahead in the research paper.
The activation function used is tanh. ‘I’ represent the
inputs given to the system. ‘X’ represents the values after
the application of activation function and ‘w’ represents
the weights assigned.
The pre coding effort estimation consists of the three
inputs components which get inputs from the design
document. The three components are:
Actor components: This takes information about the
Figure 2. Architecture of the proposed system

Test Effort Estimation Using Neural Network 335
4.2 Pre Coding Phase Effort Estimation
The proposed Pre coding effort estimation is based on the
model proposed by Nageswaran [5].
Upon which this paper proposes a new improvement i.e.
The obtained values of UAW, UUCW, TEF and
estimated test effort are trained to the network. The
network trains itself to predict the values of weights and
threshold values for the activation levels. The network is
trained through test data over multiple iteration s.
Then the network is provided with the information for
the project for which an estimate needs to obtain. The
information is derived from the design document. The
network provides with the effort in terms of the
person-months.
4.3 Neural Network Structure for Pre Coding
Effort Estimation
1) Designing the network:
The network structure chosen for this phase involves
three layers. One input layer through which the UAW,
UUCW, TEF are given as the inputs to the network. The
hidden layer consists of three nodes which are used for
realizing the effort estimation function. The output layer
consists of one node. The out put of w hi ch gi ves the eff o rt
for the phase.
2) Training the network:
The network is trained with the test data that has been
obtained from various sources. The test data is taken
from Estimator Pal [9] and Use case Point [5] both of
which contain the test data taken from a real time project,
also we are using some of the real data for training
purpose. This data would be helpful in training the neural
network. UUCW, UAW, TEF are calculated for various
projects and their test effort is provided as the training
data. The network is trained for the data with maximum
error rate of 0.2. The network gets trained with the
provided test data over few thousands of iterations.
3) Testing the network:
The use case, actor, technical and environmental fac-
tors for the project whose test effort needs to be evalu-
ated is taken as the input and is provided to the network
which in turn provides the users with effort in per-
son-months.
The Figure 3 shows the neural network structure for
the pre coding phase effort estimation model. It is
developed in the easyneurons environment. It shows the
thresholds, activation values for input, hidden, output
nodes for the structure.
4.4 Post Coding Phase Effort Estimation
During this phase the project manager uses the coding
document to make an estimatio n of the test effort.
The proposed method is based on the fact that the test
effort is based on the number of inputs, number of
outputs, and the complexity of the code and the criti-
calness of the code.
Different weightage factors are given a value each.
Variables component: As the number of inputs in-
creases the number of test cases also increases. Different
measures are given for different types of inputs. It can be
observed from the Table 4. The method proposed makes
use of the fact that a character data type doesn’t need
more than single test data, while an integer data would
require more test cases and array variable would require
even more test cases for testing [1]. Thus the assigned
weights increase proportionately. var[i] takes the values
of number of occurrences of each variable in the order
mentioned in the Table 4. Var_comp[i] is the assigned
weights which are taken from the Table 4. Thus the va-
riable var_val is the summation of product of the number
of occurrences of variables and their assigned weights.
Complexity component:
The complexity of the code is a measure of the number
of test cases required for testing. Thus Table 5 giving a
measure for the complexity of the code is used. The as-
signed weight increases proportionately as the complex-
ity of the code increases.
Criticalness component:
The number of test cases increases proportionately
with increase in the criticalness of the system, the meas-
ure can be obtained from Table 6. The criticalness of the
code is an indication of the importance of the code. If it
is a general purpose code it is assigned a very less value
(most of the project classifies under it). However if it is
an essential mission critical code then the test effort in-
creases proportionately as the number of test cases in-
creases rapidly and thus the criticalness factor is assigned
a very high value. As illustrated in the Table 6 below.
A variable

has been defined as an intermediate
variable in measuring the effort. It is the product of
var_val value, complexity value and the critical ness value.
Table 4. Complexity assignment table for variables
Input type Assigned weight
Integer 3
Array variable 4
Character 1
Table 5. Complexity weight assignment for code
Complexity of the code Assigned weight
O(n) 1
O(log n) 2
O(nlog n) 3
O(n2) 4
O(n3) 5
O(n4) 6

Test Effort Estimation Using Neural Network 336
Figure 3. Neural network structure
Table 6. Criticalness assignment table
Criticalness of the code Assigned weight
General purpose code 1
Higher critical code 2
Mission critical code 3
Var_val = (var[i]*var_comp[i])


= var_val*complex ity*criticalness
Effort = (

+ 13.5)*10/3 (1)
The equation is arrived based on the halstead effort
estimation model. The effort is estimated on a large
number of test cases (the test cases here being the source
codes of quick sort, bubble sort, gcd program etc.,) the
halstead effort is estimated for the test cases, the effort is
obtained in elementary mental discriminations. For the
same test cases the value of

is computed and a large
pool of values for the comparison of the proposed
variable and the halstead estimated effort is obtained.
The constant 13.5 and the multiplying factor 10/3 have
been arrived from this large pool of values and their
comparisons.
A relation is obtained for the obtained

values and
the estimated val ues. Thus E q uat i on (1 ) has been derived.
The obtained values var_val and

and estimated test
effort according to the proposed model passed as training
set to the network. The network trains itself to predict th e
values of weights and threshold values for the activation
levels. The network is trained through test data over
multiple iterations.
Then the network is provided with the information for
the project for which an estimate needs to be obtained.
The information is derived from the source code
document. The various parameters are estimated from the
source code like the variable occurrences, complexity of
the code etc. The network provides with the effort in
terms of the elementary mental discriminations (as the
formula was derived using the Halstead model). The
network gets trained with the proposed effort estimation
function for the post coding phase.
4.5 Neural Network Structure for Post Coding
Effort Estimation
1) Designing the network:
The designing of the network involves the selection of
the network architecture. The architecture is chosen in
such a way that it is in accordance with the proposed
effort estimation function. The proposed effort estimation
function for the post coding phase implies the design of
the network structure with two input nodes, two hidden
nodes and one output node. The two input nodes are
provided with the values of var_val and

at input

Test Effort Estimation Using Neural Network337
layer. The network gives effort in terms of the EMDs on
the output layer which consists of only one node, the
output nod e.
2) Training the network:
Training the network involves the compilation of the
test data: the test data has been obtained by manually
calculating the proposed model effort, var_val,

,
halstead effort for a substantial number of program codes.
The training set is provided to the network designed as
above. The acceptable error rate is set to 0.1. The
network is trained with the compiled test data and the
network converges over a period of thousands of
iterations.
3) Testing the network:
The proposed model accepts the number of variables
and their occurrences, complexity of the code, critical-
ness of the code as the input and it computes the values
of var_val,

and provides it to the network. The net-
work calculates the estimated effort according to the
proposed evolved model and produces an output in terms
of elementary mental discriminations (EMD).
Figure 4 shows the neural network structure which
has been obtained using the easyneurons freeware
application. The figure shows the network structure, the
thresholds, and the activation levels on various nodes.
The model developed takes the inputs from the users
(project managers) estimates the intermediate values,
passes it to neural network structure which was realized
and retrieves the information from it and passes it to the
model which then evolves the data to provide with the
estimated effort as the final output.
5. Application of Proposed Model to Test
Cases
The proposed model which has the effort estimation in
pre coding phase in person-months and in post coding
phase in elementary mental discriminations has been
applied to various project data. The data has been
obtained from Estimator Pal, Usecase point [14] which
has a detailed design report. It has also been applied to
other minor projects.
The post estimation model is very cumbersome. It has
been applied to obtain the proposed estimated value as
well as the value that is obtained from Halstead model.
6. Results and Discussion
The model has been applied to various projects as men-
tioned above. The following are the results obtained.
Figure 5 gives the comparison of pre code effort esti-
Figure 4. Neural network structure

Test Effort Estimation Using Neural Network 338
efforts
Figure 5. Comparison of pre code effort estimation
Project number
efforts
Project number
Figure 6. Comparison of post code effort estimations
mations. The X-axis represents the number of the test
case and the Y-axis represents the effort interms of
person-months. Series1 represents the test effort for pre-
coding effort estimation based on the proposed model,
while the Series2 represents the pre code effort estimation
based on a traditional method. The method to which the
proposed method is being compared to is [5] effort
estimation based on usecase.
Careful analysis of the results obtained provides the
information that the proposed estimation has a deviation
of about 8% over the traditional method that has been
chosen. This deviation is not much considering the fact
that the effort estimated by the traditional method has
also not being found to be accurate when applied to real
time projects. The method based on usecase points [5]
and several other traditional methods haven’t produced
an accurate estimate of the test effort. The proposed
method has been applied on real time data from few of
the projects that have been specified above and it has
been found to produce an estimate of about 8% deviation
from the mentioned effort.
The interpretation of the results obtained and men-
tioned in the above graph indicate another fact, that the
estimated effort has been found to be always on the
higher side of traditional method. The deviation found
here is found to be on the positive sid e.
When the results were analyzed with the real time data
the proposed model has been found to be more accurate
than the traditional method that has been chosen. The
proposed model estimated the effort more accurately.
Figure 6 shows the comparison of effort estimation
for post coding phase. The X-axis represents the number
of the project and the Y-axis gives the effort estimation
in terms of elementary mental discriminations. Series1
represents the test effort estimation based on the
proposed model which was evolved from the halstead
model, cyclomatic model [7] and the application of
neural network. Series represents the test effort esti-
mation based on the Halstead model [6].
It can be observed from the graph and the analysis of
the results which were obtained by applying the proposed
model over several projects that there is about 10%
deviation in the test effort estimation for halstead model.
The model has been applied to various projects men-
tioned as above for post effort estimation.
The deviation has also been found to be varying and it
has been seen that it is both on th e positive side and neg-
ative side of the halstead effort. It can be observed

Test Effort Estimation Using Neural Network 339
efforts
Figure 7. Comparison of pre and post code effort estimations, along with th e conventional estimates
Project number
that the cyclomatic complexity model [7] and the
halstead model [6] haven’t been able to estimate the
effort accurately. In general there has not been any model
that could estimate the effort estimation accurat el y.
There is no accurate effort estimation for post coding
phase. The proposed model has produced results which
are in synchronization with the actual effort estimations
and found to be more accurate.
In Figure 7 the comparison of pre and post code effort
estimations is given. The model developed has been
applied to some student projects and the graph is plotted.
In the figure the X axis represents the project number and
Y-axis represents the effort. Series1 indicates the pre
coding test effort for the proposed model and Series2
represents traditional method pre coding effort estimation,
Series3 represents the post coding test effort for the pro-
posed model and Series4 represents the traditional me-
thod post coding test effort estimation. It is showing a
variation of about 8% over large number of projects.
Thus it confirms the fact the estimated efforts both in pre
and post coding phase have higher accuracy than the
conventional models which as shown earlier show large
deviation.
7. Conclusions
The models used for the traditional pre coding effort
estimations use the usecase point or the function point.
The paper has covered brief details of the various
traditional methods for effort estimations both in pre
coding phase and in post coding phase. It then had the
introduction of various keywords which are a part of the
proposed model.
The proposed effort estimation models for pre coding
phase based on usecase point and soft computing
technique- neural network has been applied to improve
upon the accuracy. The method that has been followed
and the metric proposed have an advantage that it
produces accurate results. For the post coding effort
estimation the proposed model estimated the effort based
on and used neural network to improve upon accuracy
and the results have been found to show that the
proposed estimation is in synchronization with the
traditional effort estimation models.
The future scope for the proposed model is based in
the direction that the model developed needs to be ap-
plied to large number of test cases i.e., real time projects
as the proposed model has a unique feature of learning
through usage. The model converges towards more ac-
curate values as it used over time. The model developed
can be evolved even further in the view that more num-
ber of parameters which have a minor effect on the effort
estimation be also considered for effort estimation and
the model can be evolved.
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