17-03-2014, 10:21 PM
Abstract—Software engineering is continuously facing the
challenges of growing complexity of software packages and
increased level of data on defects and drawbacks from
software production process. This makes a clarion call for
inventions and methods which can enable a more reusable,
reliable, easily maintainable and high quality software systems
with deeper control on software generation process. Quality and
productivity are indeed the two most important parameters for
controlling any industrial process. Implementation of a
successful control system requires some means of
measurement. Software metrics play an important role in the
management aspects of the software development process such
as better planning, assessment of improvements, resource
allocation and reduction of unpredictability. The process
involving early detection of potential problems, productivity
evaluation and evaluating external quality factors such as
reusability, maintainability, defect proneness and complexity
are of utmost importance. Here we discuss the application of
CK metrics and estimation model to predict the external
quality parameters for optimizing the design process and
production process for desired levels of quality. Estimation of
defect-proneness in object-oriented system at design level is
developed using a novel methodology where models of
relationship between CK metrics and defect-proneness index is
achieved. A multifunctional estimation approach captures the
correlation between CK metrics and defect proneness level of
software modules.
Keywords-Quality, design, defect-proneness, internal
parameters, metrics, DIT, RFC, WMC, Estimation.
I. INTRODUCTION
In the object-oriented environment, one of the major
aspects having strong influence on the quality of resulting
software system is the design complexity. The structural
property of the software component is influenced by the
cognitive properties of the individuals involved in
designing, development and testing, and it will be reflected
in the structural properties of the developed software. The
OO paradigm offers the technology to create components
that can be used for generic programming. CK metrics suite
[2] is one of the object-oriented design complexity
measurement systems which support the measurement of
the external quality parameter which may evolve in
software package. The literature widely refers to the metric
suite which depends on the internal structural analysis of
object-oriented components such as inheritance, coupling,
cohesion, method invocation, and association [ 4, 5, 6].
It is found that there exists a complex functional
relationship between the internal design qualities and the
defect proneness of the system. The model arrived in this
work predicts the defect proneness index of software based
on the three factors such as Depth of Inheritance (DIT),
Response For a Class (RFC) and Weighted Method per
Class (WMC) of CK metric suite. The method of
implementation of this approach involves the analysis and
estimation of functional relationship presenting the system
properties which offers external parameters like defect
proneness. A set of wider observation on real world values
of external quality parameters with selected CK metrics
have offered the relationship. Once relationship has been
established through various functional estimation
techniques the same relationship is used to predict defectproneness
index of classes obtained by evaluating CK
Metrics. Table I depicts the definitions and NASA–
Rosenberg thresholds of CK metrics [3].
challenges of growing complexity of software packages and
increased level of data on defects and drawbacks from
software production process. This makes a clarion call for
inventions and methods which can enable a more reusable,
reliable, easily maintainable and high quality software systems
with deeper control on software generation process. Quality and
productivity are indeed the two most important parameters for
controlling any industrial process. Implementation of a
successful control system requires some means of
measurement. Software metrics play an important role in the
management aspects of the software development process such
as better planning, assessment of improvements, resource
allocation and reduction of unpredictability. The process
involving early detection of potential problems, productivity
evaluation and evaluating external quality factors such as
reusability, maintainability, defect proneness and complexity
are of utmost importance. Here we discuss the application of
CK metrics and estimation model to predict the external
quality parameters for optimizing the design process and
production process for desired levels of quality. Estimation of
defect-proneness in object-oriented system at design level is
developed using a novel methodology where models of
relationship between CK metrics and defect-proneness index is
achieved. A multifunctional estimation approach captures the
correlation between CK metrics and defect proneness level of
software modules.
Keywords-Quality, design, defect-proneness, internal
parameters, metrics, DIT, RFC, WMC, Estimation.
I. INTRODUCTION
In the object-oriented environment, one of the major
aspects having strong influence on the quality of resulting
software system is the design complexity. The structural
property of the software component is influenced by the
cognitive properties of the individuals involved in
designing, development and testing, and it will be reflected
in the structural properties of the developed software. The
OO paradigm offers the technology to create components
that can be used for generic programming. CK metrics suite
[2] is one of the object-oriented design complexity
measurement systems which support the measurement of
the external quality parameter which may evolve in
software package. The literature widely refers to the metric
suite which depends on the internal structural analysis of
object-oriented components such as inheritance, coupling,
cohesion, method invocation, and association [ 4, 5, 6].
It is found that there exists a complex functional
relationship between the internal design qualities and the
defect proneness of the system. The model arrived in this
work predicts the defect proneness index of software based
on the three factors such as Depth of Inheritance (DIT),
Response For a Class (RFC) and Weighted Method per
Class (WMC) of CK metric suite. The method of
implementation of this approach involves the analysis and
estimation of functional relationship presenting the system
properties which offers external parameters like defect
proneness. A set of wider observation on real world values
of external quality parameters with selected CK metrics
have offered the relationship. Once relationship has been
established through various functional estimation
techniques the same relationship is used to predict defectproneness
index of classes obtained by evaluating CK
Metrics. Table I depicts the definitions and NASA–
Rosenberg thresholds of CK metrics [3].