Abstract: Polymorphism is one of the main pillars of objectoriented
paradigm. It induces hidden forms of class dependencies
which may impact software quality, resulting in higher cost factor for
comprehending, debugging, testing, and maintaining the software. In
this paper, a new cognitive complexity metric called Cognitive
Weighted Polymorphism Factor (CWPF) is proposed. Apart from the
software structural complexity, it includes the cognitive complexity
on the basis of type. The cognitive weights are calibrated based on 27
empirical studies with 120 persons. A case study and experimentation
of the new software metric shows positive results. Further, a
comparative study is made and the correlation test has proved that
CWPF complexity metric is a better, more comprehensive, and more
realistic indicator of the software complexity than Abreu’s
Polymorphism Factor (PF) complexity metric.
Abstract: Measuring the complexity of software has been an
insoluble problem in software engineering. Complexity measures can
be used to predict critical information about testability, reliability,
and maintainability of software systems from automatic analysis of
the source code. During the past few years, many complexity
measures have been invented based on the emerging Cognitive
Informatics discipline. These software complexity measures,
including cognitive functional size, lend themselves to the approach
of the total cognitive weights of basic control structures such as loops
and branches. This paper shows that the current existing calculation
method can generate different results that are algebraically
equivalence. However, analysis of the combinatorial meanings of this
calculation method shows significant flaw of the measure, which also
explains why it does not satisfy Weyuker's properties. Based on the
findings, improvement directions, such as measures fusion, and
cumulative variable counting scheme are suggested to enhance the
effectiveness of cognitive complexity measures.
Abstract: Software maintenance and mainly software
comprehension pose the largest costs in the software lifecycle. In
order to assess the cost of software comprehension, various
complexity measures have been proposed in the literature. This paper
proposes new cognitive-spatial complexity measures, which combine
the impact of spatial as well as architectural aspect of the software to
compute the software complexity. The spatial aspect of the software
complexity is taken into account using the lexical distances (in
number of lines of code) between different program elements and the
architectural aspect of the software complexity is taken into
consideration using the cognitive weights of control structures
present in control flow of the program. The proposed measures are
evaluated using standard axiomatic frameworks and then, the
proposed measures are compared with the corresponding existing
cognitive complexity measures as well as the spatial complexity
measures for object-oriented software. This study establishes that the
proposed measures are better indicators of the cognitive effort
required for software comprehension than the other existing
complexity measures for object-oriented software.
Abstract: In modern era, the biggest challenge facing the
software industry is the upcoming of new technologies. So, the
software engineers are gearing up themselves to meet and manage
change in large software system. Also they find it difficult to deal
with software cognitive complexities. In the last few years many
metrics were proposed to measure the cognitive complexity of
software. This paper aims at a comprehensive survey of the metric of
software cognitive complexity. Some classic and efficient software
cognitive complexity metrics, such as Class Complexity (CC),
Weighted Class Complexity (WCC), Extended Weighted Class
Complexity (EWCC), Class Complexity due to Inheritance (CCI) and
Average Complexity of a program due to Inheritance (ACI), are
discussed and analyzed. The comparison and the relationship of these
metrics of software complexity are also presented.
Abstract: In general, class complexity is measured based on any
one of these factors such as Line of Codes (LOC), Functional points
(FP), Number of Methods (NOM), Number of Attributes (NOA) and so on. There are several new techniques, methods and metrics with
the different factors that are to be developed by the researchers for calculating the complexity of the class in Object Oriented (OO)
software. Earlier, Arockiam et.al has proposed a new complexity measure namely Extended Weighted Class Complexity (EWCC)
which is an extension of Weighted Class Complexity which is proposed by Mishra et.al. EWCC is the sum of cognitive weights of
attributes and methods of the class and that of the classes derived. In EWCC, a cognitive weight of each attribute is considered to be 1.
The main problem in EWCC metric is that, every attribute holds the
same value but in general, cognitive load in understanding the
different types of attributes cannot be the same. So here, we are proposing a new metric namely Attribute Weighted Class Complexity
(AWCC). In AWCC, the cognitive weights have to be assigned for the attributes which are derived from the effort needed to understand
their data types. The proposed metric has been proved to be a better
measure of complexity of class with attributes through the case studies and experiments