Flexibility a key factor to testability

International Journal of Software Engineering & Applications (IJSEA), Vol.6, No.1, January 2015
DOI : 10.5121/ijsea.2015.6108 89
FLEXIBILITY: A KEY FACTOR TO
TESTABILITY
Abdullah1
, Dr. M.H. Khan2
, Dr. Reena Srivastava3
1
Research Scholar, School of Computer Application, BBD University, Lucknow, India
2
Associate Professor, Department of C.S. E., I.E.T., Lucknow, India
3
Dean, School of Computer Application, BBD University, Lucknow, India
ABSTRACT
Testability is an important software quality factor that is ineffective if it is not available at an early stage in
the development life-cycle. It becomes more essential in the case of object oriented design. Flexibility is an
important key factor to testability analysis and measurement for delivering high class testable and
maintainable software. Flexibility is a criterion of crucial significance to software developers, designers
and the quality controllers. It constantly guides and supports to avoid wastage of resources as well as
enable the designers for continuous improvement in the development process. Flexibility is concerned with
building high quality and reliable software within the constraints of cost and time. It greatly influences
cost, quality and reliability at software evolution process. Despite the fact flexibility is vital and highly
significant aspect for software development processes, it is poorly managed. This paper focuses the need
and importance of flexibility early at design phase. A model has been proposed for flexibility measurement
of object oriented design by establishing multiple linear regression. Finally the proposed model has been
validated using experimental tryout.
KEYWORDS
Flexibility Measurement, Testability Estimation, Software Testability, Software Quality, Design Phase,
Object Oriented Design.
1. INTRODUCTION
Quality has become more important with our increasing dependence on software. In the last
decades the demand for quality in software products has been increasingly emphasized. Computer
industry has been delivering exponential improvement in cost, performance, but the problems
with software are not declining. Software still delivered is late, goes beyond budget and is full of
residual faults. As per the newest IBM report, 31% of the projects get cancelled prior to they are
completed, 53% over-run their cost estimates by an average of 189%, and for each 100 projects,
there are 94 restarts [1]. A key problem of software industry is its lack of ability to develop bug
free software. If software developers are asked to officially state that the developed software is
bug free, no software would have ever been released. Objective of software engineering is to
create high quality software in time and within budget. If a product is meeting its requirements,
we may say it is a superior quality product. The whole thing is measured with respect to
requirements and if it matches, product is a quality product.

90
Software has become vital to advancement in almost all areas of human endeavor. The ability of
programming only is no longer satisfactory to build large programs. There are major problems in
the cost, timeliness, maintenance and quality of many software products. Software engineering
has the goal of solving these problems by producing good quality, testable, maintainable
software, on time, inside budget [10]. According to software engineering principles, if the process
for development of any software is right, the chance of success of the software projects is greatly
increased [22]. To attain this objective, study has to focus in a disciplined manner on both the
quality of the product and on the process used to develop the product. However, due to increase in
price of testing and maintenance of software, objective is now changing to deliver quality
software. Software testing is an important and necessary activity of software development life
cycle for producing high quality software. Testing is very important and challenging task. The
time spent and effort required for software testing is very significant and consumes about 40% to
50% of the total cost for the entire development life cycle [3]. The most important problem during
testing is that before correcting a program (error/fault), the programmer must first trace and
understand it and it is possible with the help of its flexibility. For the same reason, traceability and
understandability is the two most important criteria of quality factor flexibility [4].
It is important that cost effective testing technique must be applied during development life cycle
and maintenance. The basic factor contributing to the development of these cost effective
techniques is the testability of program. Program testability is defined as a measure of the effort
required to satisfactorily test the program according to some well defined testing criteria [18]. To
a large perspective, testing depends on how difficult the error is to trace. Program testability and
error/fault traceability are two most important concepts: the more difficult an error is to trace, the
more difficult it is in order to be fixed. The more difficult it is to correct, the higher its testability
risk is [20]. The overall effort spent on testing not only depends on human factors, process issues,
test techniques, and test tools, but also on characteristics of the software development artifacts.
The degree to which a software artifact facilitates test tasks in a given test context is called
testability [5]. If we want to improve testability we have to trace those parts of a program that
lack testability. In view of the fact, it is clear that flexibility holds an important place as part of
testability and traceability is an important criterion of flexibility [4]. The tester can use testability
information to determine on what code to focus during testing [4]. Testability has been identified
as one of the major issues in the field of software engineering for producing high quality
software. It provides insights that are found to be very much valuable for the duration of software
design, coding, testing and quality assurance [12].
2. TESTABILITY FACTOR
Testability is a major quality factor for producing high quality software. Lack of testability
contributes to increased test and maintenance effort. The IEEE Standard Glossary defines
testability as the degree to which a system or component makes possible the establishment of test
criteria and performance of tests to conclude whether those criteria have been met [24]. ISO
defines it in a parallel way: an attributes of software that bear on the effort needed to validate the
software product [25]. The most well-known definition of testability is easiness of performing
testing [13, 14]. The insight provided by software testability is significant for the extent of
development life cycle, and quality promise. Design-for-testability is a very important issue in
software engineering.
Testability is one of the most important factors determining the time and effort required to test
software system [2]. A lower degree of testability outcome means increased test effort. It is
essential in the case of OO designs where control flows are normally not hierarchical; it is costly

91
to redesign a system during implementation or maintenance. An effort has been put forth to
recognize the key factors having positive impact on testability measurement at design phase. It
has been concluded that Flexibility and Modifiability are the two most important factor affecting
software testability measurement at design phase [8]. Taking into consideration the significance
of their involvement, in this paper we have proposed a model to measure software flexibility at
design phase.
3. FLEXIBILITY
In general, the objective of flexibility is to improve the quality of software systems within the
development life cycle. Flexibility is a key concern to guarantee stability between software
artifacts of succeeding phases of the development cycle. On the other hand only some works have
addressed the theme of flexibility into its design. Flexibility is a testability factor, which allows
the incorporation of changes in a design and gives an early focus to produce testable software
within time and budget. The reported experience suggests that by emphasizing flexibility as a
quality factor for testability measurement from the initial stage, the documentation will be clearer
and extra reliable [16]. Flexibility of software artifacts has been recognized as an important key
factor of testability for supporting various activities in the software system development process.
More purposely, flexibility information can be used to support the analysis of implications and
integration of changes that occurs in software systems. Flexibility enables system acceptance by
allowing users to better understand the system and contributes to clear and consistent system
documentation. Researchers and Practitioners advocated that flexibility aspect of software is
highly desirable and significant for developing quality software. Literature survey reveals that
there are various aspects of software, including flexibility factor that either directly or indirectly
influence testability of software [4], [6], [7]. Flexibility plays a central role for producing quality
software; it incorporates software quality objectives into the product and focuses on early error
detection and design flaws. Aforementioned facts reveal that flexibility is a key factor to
testability.
3.1. FLEXIBILITY QUALITY CRITERIA
Criteria are the characteristics which classify the quality factors. The criteria of the factors are the
attributes of the software product or software production process by which the factor can be
judged or characterized. The relationship between the flexibility and the quality criteria is listed
below in Table 1.

92
Table 1: Flexibility Quality Criteria
S.No. Factor Quality Criteria Mode
1
Flexibility
Structured
Augment ability
Criteria of Boehm quality
Mode
2 Generality
Independence
Self- documentation
Modularity
Software independence
Criteria of McCall quality
Mode
3 Complexity
Concision
Consistency
Generality
Modularity
Self-documentation
Expandability
Simplicity
Criteria of Ming-Chang
Lee Mode
There are following four major motivations for developing a list of criteria for flexibility:
1. Criteria provide a more absolute and real definition of factors.
2. Criteria common between factors help to show the interrelation among factors.
3. Criteria allow assessment and review metrics to be developed with greater easiness.
4. Criteria consent to identify that area of quality factors which may not be up to a predefined
acceptable standard.
4. OBJECT ORIENTED DESIGN PROPERTIES
Object oriented design is the latest approach and provides a set of methods, tools and procedures
for development of quality software. Object oriented design properties are real concepts that can
be directly judged by scrutinizing the internal and external organization, association, and
functionality of the design components, classes, attributes and methods. The feature of a design
has a very important impact on the quality of a software product; but due to the multiplicity and
complexity of design properties (e.g., coupling, encapsulation), their measurement and correlation
with external quality attributes (e.g., testability, portability) is tough. Object-oriented design
argues to continue essential software quality objectives like testability and reusability by
mechanisms similar to encapsulation of data, inheritance and dynamic binding. Object-oriented
programming is a fundamental technology that supports the above mentioned quality goals but
only knowing the language policy or concepts of the object-oriented technology is not sufficient
to produce good quality software [21]. A good quality object-oriented design wants design policy
and practices that must be known and used. Their contravention will finally have a strong impact
on the higher-level quality attributes. But as mentioned in advance, quality must be expressed in a
quantified manner [15].
The design properties of abstraction, encapsulation, coupling, cohesion, complexity and design
size are regularly used as being representative of design quality characteristics in cooperation
with procedural development as well as object oriented development. Messaging, composition,

93
inheritance, polymorphism and class hierarchies correspond to new design concepts which have
been introduced by the object oriented paradigm and are thus vital to the quality of an object
oriented design [17]. Table 2 summarizes the various design properties and their benefits.
Table 2: Object Oriented Design Properties and Their Benefits
Design
Property
Definition Benefits
Design Size A measure of the number of classes
used in a design.
Representative of design quality
characteristics of object oriented
development
Abstraction A measure of the generalization,
specialization view of the design.
Flexibility
Effectiveness
Functionality
Encapsulation Hides the implementation details. Reduces complexity
Flexibility
Understandability
Easier testing and maintenance
Coupling Defines the interdependency of an
object on other objects in a design.
Low Coupling Provides
Flexibility
Good understandability
Cohesion Assesses the relatedness of methods
and attributes in a class.
Flexibility
Inheritance Allows child classes inherit the
characteristics of existing parent
class.
Flexibility
Reusability
Eliminates redundant code
Polymorphism Ability to take more than one form.
(Extensively used in implementing
inheritance)
Provide abstraction
Extensibility
Reusability
Eliminates redundant code
Messaging A count of the number of public
methods that is available as services
to other classes. This is a measure of
the services that a class provides.
Functionality
Testability
Effectiveness
Reusability
5. OBJECT ORIENTED METRICS
Object orientation has several advantages such as lower development effort and lower
development time and better testability, flexibility and maintainability etc. It is based on objects
that constitute a system. It is more close to the real world. Object oriented approach supported
some very useful design principles like maintainability, testability, reusability and quality. Each
of the design properties identified in this paper signify an attribute or characteristics of a design
that is satisfactorily well defined to be objectively assessed by using one or more well defined
design metrics during the design stage. A review of existing design metrics [19] revealed that
there are numerous metrics that can be modified and used in the estimation of some design
properties, such as abstraction, inheritance and messaging. After a thorough review of some of the
existing object-oriented metrics those might be applied in the design phase of object-oriented

94
software systems, a set of metrics listed in [17] has been elected for measuring flexibility at
design phase.
6. RELATIONSHIP OF FLEXIBILITY WITH DESIGN PROPERTIES
Many experts tried to incorporate a variety of views as to how design properties may influence
design quality attributes and establish a strong relationship between object oriented design
properties and quality attribute flexibility. An extensive review of object oriented design and
development was conducted in order to develop a basis for relating design properties to quality
attribute flexibility [4]. It was observed that each of these properties have positive or negative
impact on the factors that influence testability of object oriented design. After an in-depth
evaluation of available literature on the topic [5] [9] [10] [11] [12] [15] [17], we established a
correlation among object oriented design properties and flexibility as shown in Figure 1.
Figure 1: Correlation among Object Oriented Design Properties and Flexibility
Table 3: Metrics Description
Metric Description
DAM Data Access Metrics
DCC Direct Class Coupling
CAM Cohesion Among Methods of Class
MFA Measure of Functional Abstraction
7. FLEXIBILITY MEASUREMENT MODEL
As discussed in the previous section, we have established the correlation among flexibility, object
oriented design properties and object oriented metrics as depicted in Figure 1. With this
correlation, this paper proposes a model for flexibility measurement. The proposed model has
been developed using multiple linear regression procedure. This regression procedure has been
widely accepted [8, 10, 26]. With the established correlation and linear regression we can say that
Flexibility= ß + A1 × Encapsulation + A2 × Coupling + A3 × Cohesion + A4 × Inheritance
Eq. (1)
We used SPSS to calculate the coefficients and the final flexibility model that we arrived at is
Flexibility= 1.051 + 2.320 × Encapsulation + 0.160 × Coupling - 2.283 × Cohesion + 11.572 ×
Inheritance Eq. (2)

95
Table 4: Coefficients
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 1.051 10.446 0.101 0.926
Encapsulation 2.320 10.433 0.275 0.222 0.838
Coupling 0.160 0.752 0.147 0.212 0.845
Cohesion -2.283 3.298 -0.846 -0.692 0.539
Inheritance 11.572 5.869 0.810 1.972 0.143
a. Dependent Variable: Flexibility
The Coefficients part of the output gives us the values that we need in order to write the
regression equation (1). The Standardized Beta Coefficients give a measure of the contribution of
each variable to the flexibility model. A big value designates that a unit change in this predictor
variable has a large effect on the criterion variable. The t and Sig (p) values give a rough
indication of the impact of each predictor variable – a big absolute T value and small p value
suggests that a predictor variable is having a large impact on the criterion variable. The
experimental evaluation of flexibility is very encouraging to obtain testability index of software
design for low cost testing and maintenance.
The data used for developing flexibility model is taken from [21] that have been collected through
large commercial object oriented systems. The index value of the expert’s rating of flexibility for
these systems is known and is termed as ‘Known Value’ in this research paper. For experimental
validation this ’Known Value’ will be compared with the calculated value; values will be
calculated with the help of the proposed model.
The descriptive statistics of the output gives the mean, standard deviation, and observation count
(N) for each of the dependent and independent variables and is shown in Table 5.
Table 5: Descriptive Statistics
Mean Std. Deviation N
Flexibility 7.1938 2.88881 8
Encapsulation 0.9013 0.34203 8
Coupling 3.0775 2.65602 8
Cohesion 0.7663 1.07103 8
Inheritance 0.4588 0.20223 8
The Model Summary table of the output is most useful when performing multiple regression.
Capital R is the multiple correlation coefficients that tell us how strongly the multiple
independent variables are related to the dependent variable. R square is very supportive as it gives
us the coefficient of determination. The Model Summary is shown in Table 6.

96
Table 6: Model Summary
Mode
l
R R Square Adjusted R
Square
Std. Error of the Estimate
1 0.903a
0.816 0.571 1.89306
a. Predictors: (Constant), Inheritance, Cohesion, Coupling, Encapsulation
8. Empirical Validation
The empirical validation is an essential stage of planned research. Empirical validation is the
correct approach and practice to justify the model acceptance. Keeping view of this fact, practical
validation of the Flexibility measurement model has been performed using sample tryouts. In
order to validate proposed flexibility measurement model the data has been taken from [21].
During experiments, flexibility value of the projects has been calculated using the developed
model, followed by the calculation of flexibility rating. These calculated ratings are then
compared with the known rating given by experts with the help of Charles Speraman’s
Coefficient of Correlation.
Table 7: Known and Calculated Flexibility Rating
Projects Known
Flexibility
Value
Known
Flexibility
Rating
Calculated
Flexibility
Value
Calculated
Flexibility
Rating
P1 8.22 11 8.36 11
P2 12.64 14 9.29 14
P3 12.94 15 9.13 13
P4 0.84 1 7.09 8
P5 1.5 2 6.53 7
P6 3.0 3 6.19 5
P7 7.2 9 4.79 3
P8 6.2 6 5.83 4
P9 4.2 4 10.79 15
P10 4.7 5 7.24 9
P11 8.2 10 8.67 12
P12 9.5 13 7.83 10
P13 8.5 12 3.56 2
P14 7.2 8 6.43 6
P15 6.7 7 1.40 1

97
Table 8: Computed Ranking, Actual Ranking and their Relation
Projects
Flexibility
Ranking
P
1
P
2
P
3
P
4
P
5
P
6
P
7
P
8
P
9
P1
0
P1
1
P1
2
P1
3
P1
4
P1
5
Computed
Ranking
1
1
14 13 8 7 5 3 4 15 9 12 10 2 6 1
Known
Ranking
1
1
14 15 1 2 3 9 6 4 5 10 13 12 8 7
∑d2
0 0 4 49 25 4 36 4 12
1
16 4 9 10
0
4 36
rs 1 1 .9
9
.9
1
.9
6
.9
9
.9
4
99 .7
8
.97 .99 .98 .82 .99 .94
rs >.6536
rs >.6536 means significant results.
Flexibility measurement model had statistically significant correlations with 15 of 15
projects.
As mentioned above, Charles Speraman’s Coefficient of Correlation (rank relation) rs was used
to check the significance of correlation between calculated values of flexibility using model and
it’s ‘Known Values’. Rank correlation is the process of determining the degree of correlation
between two variables. The ‘rs’ was calculated using the method given as under: Speraman’s
Coefficient of Correlation
‘d’ = difference between ‘Calculated values’ and ‘Known values’ of Flexibility.
n = number of projects (n=15) used in the experiment.
The correlation values between flexibility through model and known ranking are shown in table
above. Pairs of these values with correlation values rs above [±.6536] are checked in Table 8. The
correlations are up to standard with high degree of confidence, i.e. up to 99%. Therefore we can
conclude without any loss of generality that flexibility measurement model measures are really
reliable and significant.
9. Conclusion
Flexibility is one of the most significant factors for measuring testability of object oriented
software design. Study developed a flexibility measurement model that establishes the correlation
among flexibility, object oriented design properties and object oriented metrics. This paper
shows the significance of flexibility as a key factor of testability and its relationship with various
object oriented design properties. Flexibility measurement model in design phase has been
developed and validated theoretically as well as empirically using experimental try-out. For
experimental validation several large commercial projects has been used. The applied validation
on the flexibility model concludes that developed model is highly reliable, up to standard and

98
significant. Finally paper concludes there is a high corelation between flexibility and testability,
in addition statistical results shows that flexibility model is statistically very much significant and
reliable.
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Authors
Abdullah received the MCA degree from Uttar Pradesh Technical University, Lucknow, in
2006. He is currently working as an Associate Professor, in the Department of Computer
Application, at Institute of Environment and Management, Lucknow. His research interests
Include Software testability, Software Quality Estimation. He has written various books and
study materials for (North Orissa University) Orissa, (Suresh Gyan Vihar University, Jaipur) Rajasthan,
(Bharati Vidyapeeth University, Pune) Maharashtra, NAAC Re-Accredited “A" Grade University.
Dr. Reena Srivastava is currently working as Dean, School of Computer Applications at
BBD University, Lucknow. She received her Ph.D. degree from MNNIT Allahabad, India.
Her research area includes Multi-Relational Classification, Privacy Preserving Data Mining
and Software Engineering.
Dr. M. H. Khan, Associate Professor, Department of Computer Science and Engineering at
IET Lucknow UP. He obtained his MCA degree from Aligarh Muslim University (A Central
University) in 1989. Later he did his PhD from Lucknow University. He has around 25 years
rich teaching experience at UG and PG level. His area of research is Software Engineering. Dr.
Khan published numerous articles, several papers in the National and International Journals and conference
proceedings.

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