Cognitive Complexity Measures: An Analysis

Cognitive Complexity Measures: An Analysis

Sanjay Misra (Federal University of Technology, Nigeria)
DOI: 10.4018/978-1-61350-456-7.ch705
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Abstract

Cognitive informatics (CI), a multidisciplinary area of research tries to solve the common problems in the field of informatics, computer science, software engineering, mathematics, cognitive science, neurobiology, psychology, and physiology. Measurement in software engineering is also a core issue which is still striving for its standardization process. In recent years, several cognitive complexity measures based on CI have been proposed. However, each of them has their own advantages and disadvantages. This chapter presents a critical review on existing cognitive complexity measures. Furthermore, a comparative study based on some selected attributes has been presented.
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Introduction

Software metrics play an important role in the software development process, since; they assist the software developer in assuring the software quality. Developers use these metrics in the entire life cycle of software development to better understand and assess the quality of engineered products or systems that they built. On the other hand, it is a common observation that it is not possible to develop the absolute measure (Fenton, 1994). Instead, software engineers attempt to derive a set of indirect measures that lead to metrics that provide an indication of quality of some representation of software. The quality objectives may be listed as performance, reliability, availability and maintainability (Somerville, 2001) and are closely related to software complexity.

IEEE (1990) defines software complexity as “the degree to which a system or component has a design or implementation that is difficult to understand and verify”. Further, there are two categories of software complexities: computational and psychological (Fenton, 1997). Computational complexity is related to algorithm complexity and evaluates time and memory needed to execute a program. The second is psychological complexity also called as cognitive complexity is concerned to evaluate the human effort needed to perform a software task. This definition is also related to understandability of the software and task and evaluates the difficulty experienced in comprehending and/or performing such a task. Further, there are several definitions of the cognitive complexity. Henderson-Sellers (1996) define the cognitive complexity as ‘The cognitive complexity of software refers to those characteristics of software that affect the level of resources used by a person performing a given task on it.’ Fenton (1997) defines cognitive complexity as it measures the effort required to understand the software. Zuse (1998) definition of complexity also represents the notion of cognitive complexity, which states that software complexity is the difficulty to maintain, change and understand software. Here, it is worth mentioning that metrics and measures are often used as synonyms terms in software engineering. It is because of the fact that both terms have approximately similar definitions. Pressman (2001) explains the measure in software engineering context as ‘a measure provides a quantitative indication of the extent, amount, dimension, capacity, or size of some attributes of a product or process’. A metric is defined by IEEE as ‘a quantitative measure of the degree to which a system, component, or process possesses a given attribute’’.

Cognitive informatics (CI), a multidisciplinary area of research is emerging. It includes the researches in the field of informatics, computer science, software engineering, mathematics, cognitive science, neurobiology, psychology, and physiology (Wang, 2002, 2004, 2005, 2006, 2007, 2009). The importance of the researches in CI is due to the fact that, it tries to solve the common problems of two related area in a bi-directional and multidisciplinary approach (Wang, 2004). CI uses the computing technique to solve the problem of cognitive science, neurobiology, psychology, and physiology and on the other hand the theories of cognitive science, neurobiology, psychology, and physiology are used to investigate the issues and their solution in informatics, computing, and software engineering. For examples, the measurement in software engineering is a major issue which is still not mature and needs a lot of efforts to standardize it (i.e. the measurement techniques for software engineering). In last few years numbers of researchers have tried to solve these problems by combining the principles of cognitive science and (measurement in) software engineering. The numbers of proposals of cognitive complexity measures (Shao & Wang, 2003; Misra, 2006, 2007, 2010; Kushvaha & Misra, 2006; Auprasert & Limpiyakorn, 2008, 2009) are the results of these efforts.

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