Journal of Software Engineering and Applications, 2011, 4, 550-557
doi:10.4236/jsea.2011.49064 Published Online September 2011 (
Copyright © 2011 SciRes. JSEA
Towards Multi-Faceted Test Cases Optimization
Manoj Kumar1, Arun Sharma2, Rajesh Kumar3
1Department of Computer Application, Galgotias University, Greater Noida, India; 2Department of Computer Sc., Krishna Institute
of Engineering and Technology (KIET), Ghaziabad, India; 3School of Mathematics & Computer Application, Thapar University,
Patiala, India.
Received July 20th, 2011; revised August 23rd, 2011; accepted August 31st, 2011.
The target of software engineering is to produce high quality software product at low cost. Software testing is la-
bour-intensive, ambiguous and error prone activity of software development. How to provide cost-effective strategies
for software test cases op timization problem such as classification , minimization, selection, and prioritization ha s been
one of the research focuses in software testing for a long time. Many researchers and academicians have addressed the
effectiveness/fitness and optimiza tion of test cases, and obtained many interesting results. However, one issue of para-
mount importance in softwar e testing i.e. the intrinsic imprecise and uncertain ty of test cases fitness, fitness parameters,
multi-objective optimization, is left unaddressed. Test cases fitness depends on several parameters. Vagueness of fitness
of test cases and their fitness parameters have created the uncertainty in test cases optimization. Cost and adequacy
values are incorpo rated into multi-faceted optimization of test cases. This paper argues test cases optimiza tion requires
multi-faceted optimiza tion in order to adequately cater realistic software testing. In this paper, author s have identified
several parameters for test cases fitness and multiple objectives for test cases optimization. In addition above, authors
have formulated the test ca ses optimiza tion pro blem in three different wa ys using multi-faceted con cept. These formula-
tions can be us ed in fut u re by au thors and re sea rchers.
Keywords: Multi-Faceted Optimization, Test Cases, Test Data Adequacy Criteria, Test Case Fitness
1. Introduction
Software testing is the process of exercising the pro-
grams with specific intend of finding errors prior to de-
liver. Although software testing is a very human-inten-
sive, time consuming and itself an expensive activity, yet
launching of software without proper testing may lead to
cost potentially much higher than that of testing, spe-
cially in systems where human safety is involved [1,2].
Test cases are the inputs to the program under test. A test
case is a set of conditions or variables under which a
tester will determine whether an application or software
system is working correctly or not. Test cases pool may
contain some redundant, irrelevant and unfit test cases.
Since, testing is very expensive activity, unnecessary
execution of redundant, irrelevant and unfit test cases
will increase unnecessary burden of cost. The solution is
to choose the fittest test cases and removing the unfit,
redundant unnecessary ones, which in turn leads to test
cases optimization [3-5]. Measuring fitness of test cases
is always a daunting task. The term “fitness” refers to the
appropriateness of test cases to check the quality of soft-
ware. A test suite is a collection of test cases that are
intended to be used to test a software program to show
that it has some specified set of behaviours. Multi-criteria
test cases fitness evaluation, multi-objective test cases
optimization may be the crucial problem for next genera-
tion software testing sorority. It requires to device next
generation technologies to solve multi-faceted test cases
optimization problem. The effectiveness of this verifica-
tion and validation process depends upon the number of
errors found and rectified before releasing the system.
This, in turn, depends upon the fitness and number of test
cases exercised. So, test cases optimization is necessary.
An optimization problem is the problem of finding the
best solution from all feasible solutions. Multi-objective
optimization (MO) also known as multi-criteria or multi-
faceted or multi-attribute optimization is the process of
simultaneously optimizing two or more conflicting ob-
jectives subject to certain constraints. The objective of
MO optimization is to find the set of acceptable solutions
and present them to the decision maker to take decision.
Test cases optimization is the problem of finding the best
subset (class) of test cases from a pool of test cases to be
Towards Multi-Faceted Test Cases Optimization551
audited. It will meet all the objectives of testing concur-
rently. Most of the researchers evaluated the fitness of
test cases only on single parameter fault detecting capa-
bility. Though, the fitness of test case depends on several
parameters but consideration of only one parameter is not
appropriate. Though, there are several objectives of test
case optimization such as execution time, cost of data,
cost of risks, and coveragebility, discussed in details in
Section 3. But some objectives of test cases optimization
are conflicting in nature, coveragebility of one objective
will suffer other objective like cost, fitness of test cases
and number of test cases in suite while considering all
objectives concurrently. However most of test cases op-
timization approaches found in the literature are single
objective and focused on the problem of maximizing the
attainment of adequacy value without taking cost into
account. It was not until 2001 that detailed empirical
study was presented taking cost into account [6,7]. It was
single objective optimization of test cases incorporating
coveragebility and cost in ratio. Two objective test case
optimization can be solved in such manner. If we want to
optimize the test cases for three or more objectives, this
approach is not fruitful. So it is not appropriate to estimate
fitness of test cases just on single parameter and classify,
select, prioritize the test cases on single objective. Single
objective formulation of test cases fitness and optimiza-
tion are not meeting the objectives of testing. Test cases
should be optimized in such a way that it will achieve
maximum of code coverage, maximum requirements
coverage, high fault detecting capability, maximum mu-
tant killing score and so far. The objective of test cases
optimization is to reduce the number of test cases in suite
to be audited and improve the effectiveness/fitness of test
cases. So, test cases fitness evaluation, classification and
selection of test cases should be treated as multi-faceted
concept. It will surely reduce the cost & efforts of soft-
ware testing and improve the quality of testing and re-
duce the number of test cases to be audited also.
2. Single Objective Formulation for Test
Cases Optimization
This section briefly reviews test cases minimization, se-
lection, classification, and prioritization, which collec-
tively form part of the more general topic of test cases
optimization. This section formalizes relationship be-
tween test cases classification, selection, minimization,
prioritization and filtration that form the test cases opti-
mization. Test cases minimization is a selection of
smallest subset the test cases from a pool of test cases to
be audited for a program. It covers as many program
elements as the entire pool does. Test suite reduction
seeks to reduce the number of test cases in a test suite
while retaining a high percentage of the original suite’s
fault detection effectiveness. Test suite minimization
techniques seek to reduce the effort required for regres-
sion testing by selecting an appropriate subset of test
suite. Test suite minimization is minimal set cover prob-
lem, which uses greedy approximation approach to solve
it. So, Test suite minimization is NP-complete problem
[8,9]. Peculiar nature problems are those problems which
require curious mix of data and knowledge driven ap-
proach to solve it. In search based software engineering,
test cases optimization is a search space problem, which
requires hybridization of data driven and knowledge
driven approach to find near optimal solution of the
problem. Hence, Test cases optimization is also peculiar
nature problem [6,10]. Test cases selection is also finding
minimal cardinality subset of test cases from the pool of
test cases. One major difference between test cases
minimization and test case selection is that test case se-
lection chooses a temporary subset of test cases, whereas
test suite minimization reduces the test suite permanently
based on some external criterion such as structural cov-
erage. Test case prioritization techniques try to find an
ordering/ranking of test cases, so that some test case
adequacy can be maximized as early as possible. Test
case prioritization and filtration depend on quality of
initial population of test cases. Selection and prioritiza-
tion of test cases are the two important solutions to the
problem of test case optimization. Test case filtration and
prioritization are closely related. In fact, test cases can be
filtered by selecting the first N ordered test cases. There-
fore, any test case prioritization algorithm can be used as
a test case selection algorithm. Naturally, it is desirable
to select those test cases that are most likely to reveal
defects in the program under test. When testing a pro-
gram, software testers have to define the testing objec-
tives first. A test suite is then constructed to satisfy the
all objectives of testing. It is generally agreed that a test
suite must achieve maximum coveragebility of all objec-
tives of testing [11,12]. Usually, the constructed test suite
may contain redundant test cases. A test case in a test
suite is said to be redundant if the same testing objective
can still be satisfied by other test cases of the test suite.
Since the execution of test cases and evaluation their
results are very expensive, it is of paramount importance
to remove redundant test cases within a test suite. How-
ever, removal of all redundant test cases is practically
infeasible because the problem is NP-complete. However,
a weakness of test suite reduction is that the removal of
some test cases from the test suite may potentially reduce
the fault detecting capability of the test suite too [13,14].
To be worthwhile, the sum of the cost of test cases filtra-
tion, execution and audit of selected test cases should be
less than the cost of that of all of the test cases of the
Copyright © 2011 SciRes. JSEA
Towards Multi-Faceted Test Cases Optimization
original pool. The goal of test cases filtration is to chunk/
filter out irrelevant, redundant and less fit test cases from
the test suite. Test cases filtration is to chunk out subset
of closely related test cases, so that a large portion of the
defects would be found as if the whole test suite was to
be used. It is often desirable to filter a pool of test cases
for a program in order to identify a subset that will actu-
ally be executed and audited at a particular time. When it
is uncertain that how many test cases can be run and au-
dited, it is advantageous to order or rank the test cases as
per priorities, so that the tester will select the test cases as
per their rank or order, which permit tester to start quick,
early fixing the most of the defects [11]. Single objective
formulation of test cases minimization, selection and
prioritization are as follows:
Definition 1 (Test Case Mi ni mization): Let T be a set
of test cases and R be a set of test objectives. M is a test
case minimization of T with respect to R if and only if M
is a subset of T that maximizes R(T).
Traditionally, R is defined with respect to a set
of syntactic elements of the program under
test to be covered by the test suite, such that R(X) is de-
fined to be the number of elements in
,, ,
rr r
rr r
covered by X. Typically, we seek a set M that is smaller
than T, one that is, therefore, a proper subset of T. Ideally,
M will be minimal; no other minimized test suite will be
smaller. In some approaches, we may seek to cover all
elements of , rather than merely maximiz-
ing their coverage. When R is defined in terms of cover-
age, test case minimization becomes an instance of the
set cover optimization problem. Test cases selection
problem can be formulated in terms of test cases mini-
mization problem. It becomes minimal set cover optimi-
zation problem. So, test cases minimization is NP-Com-
plete problem.
rr r
Definition 2 (Test Case Selection): Let P0 be a modi-
fied version of P and let T be a test suite. Let CA be a
function which takes a pair of programs and reports the
set of program elements of the form A that are different
in P0 compared to P. In this context, A plays the role of
test adequacy criterion, substituted by values such as
<branch adequacy> and <statement adequacy>. Let
be the number of elements of
covered when P0 is executed on X. A test set T0 is an
A-adequate test case selection of T with respect to (P, P0)
if and only if T0 is a test case minimization with respect
to .
Definition 3 (Test Case Prioritization): Let T be a
test suite containing n elements and 123
,,, ,
Tttt t
 
be a sequence on T. Let F be a function from test suite
elements to some domain on which the relation im-
poses a total order. T’ is a test case prioritization of T
with respect to F if and only if for all i, 11in
. That is, F is monotonic over T.
3. Sketch of Multi-Faceted Test Case
Tester will desire to find the subset of test cases that ac-
complish multi-objectives concurrently in order to maxi-
mize the value obtained from inherently expansive proc-
ess of executing several test cases, investigate the output
produced by them. The test problem specification in-
cludes three main parts, the purpose of testing, test cov-
erage criteria and the test strategy that will be employed.
Testing objectives can be categorized into two classes
which fall on adequacy value or fitness value (those to be
maximum) and those which fall on cost side (those to be
minimum). Minimization objective can usually be in-
verted to convert it into maximize objective. Some ob-
jectives have natural fitness function, also known as con-
straint that delimits the set of valid test cases. In search
based engineering, these constraints are treated as objec-
tives. In most realistic testing scenarios there will be at
least one cost side and one value-based objective. There
are several constraints and may be used as test objectives
[15]. Review of existing literatures have brought out
several parameters for assessing the fitness and objec-
tives of test cases optimization like maximum number of
defect detecting capability, minimum test design efforts/
cost, minimum testing cost/execution cost, maximum
coveragebility of client requirements/codes, minimum
test execution time/effort, and maximum mutant killing
score, etc are objectives and contributing as parameters
for assessing the fitness of test cases. These parameters
values/scores may be maximum/minimum according to
the test objectives category. Details of test cases fitness
evaluation parameters are given in Table 2. Contribution
of test case parameters towards their fitness is vague and
imprecise. Importance of testing objectives is also fuzzy.
So, Concurrent consideration of all fuzzy parameters and
testing objectives creates uncertainty. All fitness pa-
rameters/characteristics of test cases are not important for
each project and they are not contributing equally to fit-
ness of test cases.
3.1. Objective and Parameters Identification
Existing literatures are the evidence that there are several
objectives for test cases optimization and fitness evalua-
tion, whose details are as following (Table 1).
3.1.1. Cost-Oriented Obj ectives
The quality/fitness of the test cases is not only concern
but cost is also one of the essential criteria, the whole
purpose of test case optimization is to achieve more effi-
cient testing in terms of the cost. Selection time and exe-
Copyright © 2011 SciRes. JSEA
Towards Multi-Faceted Test Cases Optimization553
Table 1. Multi-objective test cases optimization.
S.No. Objective Category Objectives
Code coverage
Data Flow Based
Control Flow Based
Fault detection with fault Severity
Fault detection Capability
Mutation Killing Capability
Client Requirement
Execution Time
Execution Efforts
1 Fitness Oriented
Fault Localization
Cost Benefit Analysis
Execution Cost
Design Cost
Data Access Cost
Setup Cost
2 Cost Oriented
Third Party Cost
Rate of Change in Requirements
3 Project Change
Volatility Objectives Increase in Testing Efforts
4 Optimization Constraint
Table 2. Summary of multi-objective test cases optimization
Parameters Sub-Parameters
Error Seeding
Mutant Killing ability
Fault Detecting
Capability Fault Severity
Statement Coverage
Branch Coverage
Path Coverage
Loop Coverage
Relational Operator Coverage
Control Flow
Based Adequacy
Table/Array Coverage
All Definition Criteria
Data Flow Based
Adequacy All Uses Criteria
Total Efforts
Design Efforts
Selection Efforts
Execution Efforts
Requirement Change Impact
Analysis on Execution Efforts
Efforts Benefits
Total Cost
Data Access Cost
Setup Cost
Design Cost
Selection Cost
Execution Cost
Requirement Change Impact
Analysis on Execution Cost
Cost Benefits/Cost Saving
Critical Requirement Coverage
Rare Requirement Coverage
Least Requirement Coverage
Software Test Cases Fitness Evaluation Parameters
Requirement Coverage
Rate of Change in Requirements
cution time are the important factors for test cases opti-
mization. Software testing cost includes data access cost,
third party software cost, technical resources cost, setup
and simulation cost [1,2]. Execution time is a natural
candidate for test cases optimization. Execution time is
one realistic measure of effort. Physical execution time
of test cases is hard to measure accurately. Measurement
is confounded by many external factors; different hard-
ware, application software and operating system. Execu-
tion time is clearly a pressing concern for a tester, given
the short build cycles within which they will typically
have to perform the testing activities [7,16]. Data access
cost concerns to databases software. Access to databases
determines the coverage of the application under test.
The population of the database will significantly affect
the effectiveness of testing. We shall prefer realistic test
cases rather than a mocked up version of the database,
into which data may be systematically, but nonetheless
synthetically added. Such synthetic data can lead to many
false positives, because integrity constraints are not han-
dled by the automated synthetic test generation algorithm.
It can also lead to many false negatives. Data access cost
includes cost of real data population (Human cost or
payment to data provider), data retrieving cost. Some
systems interact with third parties software, creating sig-
nificant testing costs. There may be a price for accessing
the systems of a third party. However, without such third
party service access, it may not be possible to test the
system fully [17]. Embedded systems are tightly coupled
with their environment. These systems may consume
resources that have a non-trivial cost. Cost of technical
resources used in testing, is important cost driver. There
may be setup costs associated with certain test cases.
Setup cost includes cost of required devices, services,
files. However, the setup costs for the test case may be
significant in time and other costs. Such setup costs may
also introduce dependencies, leading to an interaction
between objectives and constraints. Testing automotive
software is very expensive and requires simulation. The
efforts of developing or deploying a simulation of the
real system constitute a significant cost. Cost oriented
objectives should be minimized in test case optimization
problem [15].
3.1.2. Adequacy Value-Oriented Objectives
Software test adequacy criteria are the rules to determine
whether a software system has been adequately tested,
which points out the central problem of software testing
i.e. “what is a test data adequacy criterion?” Number of
test data adequacy criteria has been proposed and inves-
tigated in the literature like code coverage or control
flow-based test adequacy criteria, data flow based ade-
quacy criteria, fault-based adequacy criteria, and er-
Copyright © 2011 SciRes. JSEA
Towards Multi-Faceted Test Cases Optimization
ror-based criteria. The control-flow based adequacy cri-
teria includes statement coverage, branch coverage, path
coverage, Length-i path coverage, loop coverage, rela-
tional operator coverage, table coverage. Data-flow
based adequacy criteria includes all definitions criterion,
all uses criterion [18,19]. Fault Sensitive model is used to
measure the fault detecting capability with fault severity.
Test cases that reveal more likely categories of faults are
more likely to be selected or prioritized. Fault History
Sensitive model is used to measure the fault detecting
capability using fault history. There is no guarantee that
past fault revelation confers an on-going value to test
cases. Such previously most fault-revealing test cases
may be regarded as “proven star performers”. Fault-
based adequacy criteria include error seeding and mutant
coverage or mutant killing score. So, fault detecting ca-
pability of test cases is very important and should be in-
corporated in test cases optimization [11,12,14]. Soft-
ware testing is human-interactive activity. Tester, project
manager, quality officer and customers are important
player for software testing, each having their own ex-
perience, knowledge, psychology and opinions on testing
priorities. Human psychological, sociological, knowledge
have high impact on software testing. Business sensitive
is highly subjective factor for testing. All features of
software are not equally important. There are also more
quantifiable business objectives. Business objectives are
key concern for test cases optimization problem. Busi-
ness Value Measurement (BVM) is a measure in which
high importance or maximum weight values are assigned
to business/customer critical requirement. Importance of
business/client requirements are the vital objectives for
test cases optimization problem. Test cases belonging to
critical business/client requirements should be executed
first [2,20].
3.1.3. Project Change Vol atility (PCV)
PCV is based on the how many times consumer is modi-
fying the project requirements during the software de-
velopment cycle. PCV is one of the criteria which help to
assess the requirement changes after the start of the im-
plementation. High PCV increases the test efforts sig-
nificantly and make it difficult to complete the project on
time. PCV is also important for test case optimization.
Test cases of high PCV value are first executed [12].
3.1.4. Mul ti -O b jective Optimi zation Constraints
Most optimization problems involve various factors in-
fluencing the optimal results. In multi-objective test
cases optimization problem, the factors that have an ef-
fect on the optimal solution are superiority, concurrency,
exclusivity, dependability of test cases. In superiority
constraint, some test cases have to be performed before
others because they establish a system state in which the
subsequent test cases become possible, or for which these
later tests perform better in some way. We may treat this
as an objective. In conjunction constraint, tests cases may
be conjoined such that executing one, entails executing
another. Such constraints may be soft (it is advantageous
to the tester to test these two together) or hard (these two
must be executed together). In exclusivity constraint, two
test cases may be mutually exclusive. For instance, if one
test completely exhausts a resource that is required by
another, then these two tests cannot both be performed.
Once again, these constraints may be soft or hard, but
where they are present, the Test cases optimization proc-
ess must take them into account. Otherwise, the test case
selection and prioritization results produced by test cases
optimization may not be viable. In dependence constraint,
Inclusion of one test case may affect the cost of another.
For instance, if we undertake the work required by a
complex setup process for a certain test case, the same
setup may be re-usable by other test cases. In this way
there may be dependence between the cost of one test
case and the costs of others. If we include one of the test
cases, then the cost of all those that remain will be re-
duced; they share the same setup procedure and the costs
associated with them [21,22].
3.2. Problem Formulations
Multi-Objective Optimization is defined as problem of
finding the vector of decision variables X which satisfies
the constraints and optimizes a vector function whose
elements represent the objective functions. Generally it
can be described as a vector function that maps a tuple of
parameters (decision variables) to a tuple of objectives.
The decision vector is also called the parameter space
and the objective vector is also called the objective space.
Find the vector *** *
xx xT
which will satisfy
the m inequality constraints: where
1, 2,,im
 and the p equality constraints
where 1, 2,,ip
 .
Test cases optimization is multi-objective, NP-Com-
plete, peculiar nature problem. Multi-objective test cases
optimization is scalable and multi-modal optimization
problem. It can be scaled to any number of objectives
and constraints. Similarly, the multi-objective formula-
tion of test optimization can be done in various ways. It
can be formulated as multiple single objective functions,
weighted sum approach, bottom-up approach and many
3.2.1. Multiple Single Objective Functions
First approach is most intuitive one and simple. In this
approach, N different single-objective functions are used
Copyright © 2011 SciRes. JSEA
Towards Multi-Faceted Test Cases Optimization555
to construct a multi-objective test cases optimization
problem. Different objective functions are simply used as
different translation of single objective function. Details
of objectives of test cases optimization are given in Ta-
bles 1-2.
Maximum/Minimum iij
Maximum 11
Minimum 22
where , and 1, 2, 3,,jm 1, 2,,in
 . N is the num-
ber of objectives and M is the number of constraints. Z1
is fault detecting capability and Z2 is execution cost of
test case. It lacks Global optimal solution. Since the test
case ti optimal for objective function Z1 is not optimal for
objective function Z2 and vice versa. It provides local
optimal solution. It requires Pareto optimal set of solu-
tions. Pareto optimal set contains individual optimal so-
lution of each objective and trade off solutions of all ob-
jectives. It requires global Pareto optimal solutions.
3.2.2. Weighted Sum Approach
Second Weighted Sum Approach is common and simple.
A set of n objectives, , and a set
of weights 123 can be combined into a
single weighted objective (WO),
OOOO O
 
112 2
** *
WO xwOxwOxwOx 
where 123 are the weight values test objec-
tives. Weight Values of test objectives can be estimated
by using Fuzzy Logic based approach and will be ex-
plored in future. Details of objectives can be found in
Tables 1-2.
www w
3.2.3. Bottom-up Approach
In bottom-up approach, test cases optimization is consid-
ered as search space problem. Pareto optimal front is
considered in this approach. Mathematical function de-
scribing Pareto optimal front is assumed in objective
space. Pareto optimality is a notion from
broad range of applications in game theory and engi-
neering. Pareto optimality provides a set of test cases not
a single test case to be exercised on software under test.
Pareto-optimal solutions are optimal in some sense.
Therefore, like single-objective optimization problems,
there exist possibilities of having both local and global
Pareto-optimal solutions. Before we define both these
types of solutions, we first discuss dominated and non-
dominated solutions.
For a problem having more than one objective func-
tion (say, Zi, and ). Any two solu-
tions x(1) and x(2) can have one of two possibilities-one
dominates the other or none dominates other. Solution x(1)
is said to be dominate other solution x(2), if both the fol-
lowing condition are true.
1, 2,,
iN 1N
1) The solution x(1) is no worse than x(2) in all objec-
2) The solution x(1) is strictly better than x(2) in at least
one objective.
If any one of above two is violated, solution x(1) does
not dominate the solution x(2). It is also true that if x(1)
dominates the solution x(2) then we can say x(2) domi-
nated by the solution x(1) or x(1) is non-dominated by
x(2) . Between two solutions x(1) is non-dominated solu-
Based on this, the
optimization prob-
lem can
the problem of finding a vector of
x, which optimize a vector of N
x where . The
functions are the mathematical description of the optimi-
zation criteria. Without the
of generality, it is
that the goal is to maximize i
1, 2,,iN
where . A
decision vector x is said to dominate a
vector y if
and only if their
1, 2,,iN
x and
1, 2,,iN
 ,
 
1, ,iN
 ,
 
vectors that are not dominated by any
other decision vector are said to form the Pareto optimal
set, while the
corresponding ob
vectors are said to
form the Pareto frontier.
Above concept can be extended to find a non-domi-
nated set of test cases form the pool of test cases. Con-
sidering the set of M test cases, each having N (N >= 1)
objective function values. In Local Pareto Optimal set, if
every member ti in test suite Ts, there exist no test case tj
which dominates any member in the test suite Ts, then
solution belonging to test suite Ts constitute local Pareto
optimal test case. In Global Pareto Optimal set, if there
exist no test case ti in test suites space which dominates
any member in the test suites space TS, then solution
belonging to test suites space TS constitute Global Pareto
optimal test case. The size and shape of Pareto Optimal
fronts usually depends on number of objective functions
and interaction among individual objective functions. If
the objective functions are conflicting in nature to each
other, resulting Pareto front may span larger than if the
objective are more cooperating. However in multi-ob-
jective test cases optimization problem, some objectives
are conflicting in nature to others. Resulting Pareto opti-
mal front of multi-objective test cases optimization may
contains many solutions, which can be found by using
multi-objective optimization algorithm. When we con-
sider the case of finding a set of non-dominated solutions
rather than a single-point solution, multi-objective Evo-
lutionary Algorithms (MOEA) have to perform a multi-
Copyright © 2011 SciRes. JSEA
Towards Multi-Faceted Test Cases Optimization
modal search for global the Pareto-optimal set.
Now the multi-objective test cases optimization prob-
lem can be defined as follows:
Definition 4 (Multi-Faceted Test Cases Optimiza-
Given: a vector of
x, and a set of
x where
1, 2,,.
Problem: Maximize
xfx by finding
the Pareto optimal set over the feasible set of solutions.
test suite minimization problem is
to select a Pareto
t subset of the
t suite, based
on multiple test criteria. It
Definition 5 (Multi-Objective Test Suite Minimiza-
Given: a test suite, T, a vector of M objective functions,
fi, .
1, 2,,
Problem: to find a subset of T, T0, such that T0 is a
Pareto optimal set with respect to the set of objective
functions, fi, . The objective functions are
the mathematical descriptions of test criteria concerned.
A subset t1 is said to dominate t2 when
1, 2,,iN
 
11 1
tft, the decision vector for t1 dominates
that of t2.
The multi-objective test case selection problem is to
select a Pareto efficient subset of the test suite, based on
multiple test criteria. It can be defined as follows:
Definition 6 (Multi-Objective Test Case Selection)
Given: a test suite, T, a vector of N objective functions,
fi, . 1, 2,,iN
Problem: to find a subset of T, , such that TT
is a
Pareto optimal set with respect to the objective functions,
fi, . The objective functions are the mathe-
matical descriptions of test criteria concerned. A subset
t1 is said to dominate t2 when the decision vector for t1
1, 2, ,
 
11 1
tft dominates that of t2.
The multi-objective test case prioritization problem is
to rank a Pareto efficient subset of the test suite, based on
multiple test criteria. It can be defined as follows:
Definition 7 (Multi-Objective Test Case Prioritiza-
Given: a test suite, T, a vector of N objective functions,
fi, . 1, 2,,iN
Problem: to find a ranks of elements of T, such that
is a Pareto optimal set with respect to the objective
functions, fi, . Let be a test suite con-
taining n elements and
1, 2, ,
iN T
Tt,,, ,tt t
be a se-
quence on T. The objective functions are the mathemati-
cal descriptions of test criteria concerned. A subset t1 is
said to dominate t2 when the decision vector for t1
 
11 1
tft dominates that of t2.
4. Conclusion and Future Scope
Testing is complex, time consuming, human-intensive,
full of uncertainty and expensive activity. Choosing the
right fit test cases is an important and critical task in
software testing. Literature is evident that there is no
direct measure of fault revelation likelihood. There are
different parameters for judging the fitness of test cases.
Cost is also an important factor for test cases optimiza-
tion involved in different ways and cannot be ignored.
This complex interplay between cost and fitness/ ade-
quacy value is further compounded by the many addi-
tional validity constraints. These constraints, cost and
fitness value are making test case minimization, selection
and prioritization problems as multi-faceted optimization
problem. The present paper advocacies that such a
Multi-faceted approach for test cases fitness evaluation
and test cases optimization is long overdue. In this paper,
authors formulated the multi-faceted test cases optimiza-
tion problem. It will be beneficial for researchers. In fu-
ture, evolutionary and soft computing techniques will be
explored for multi-faceted measurement framework for
test cases optimization and fitness evaluation.
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