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Object-Oriented Inheritance Metrics in the Context of Cognitive Complexity

Mishra D., Mishra A.

Fundamenta Informaticae

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2011

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Vol. 111, nr 1

91-117

EN

It is important to identify modules that are fault prone or exhibit evidence of high cognitive complexity as these modules require corrective actions such as increased source code inspection, refactoring or performing more exhaustive testing. This can lead to a better quality software system. It has been found that inheritance has an impact on the cognitive complexity of a software system. In this paper, two inheritance metrics based on cognitive complexity, one at class level CCI (Class Complexity due to Inheritance) and another at program level ACI (Average Complexity of a program due to Inheritance), have been proposed for object-oriented software systems. Additionally, one more metric MC (Method Complexity) has been proposed to calculate the complexity of a method. These proposed metrics are compared with some well known object-oriented inheritance metrics by calculating their values for three random C++ programs. It has been observed that CCI and ACI are better to represent cognitive complexity due to inheritance than other well known class level and program level inheritance metrics.

2

A Comparison of Metric-Based and Empirical Approaches for Cognitive Analysis of Modeling Languages

Kamandi A., Habibi J.

Fundamenta Informaticae

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2009

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Vol. 90, nr 3

337-352

EN

Modeling languages are needed to describe the conceptual construct underlying software. Several modeling languages have been proposed during the last decades. Cognitive complexity is one of the common problems in designing modeling languages. Users have to split their attention and cognitive resources between two different tasks when working with complex language: solving the problem and understanding the elements composing the language. Several researches have been accomplished to evaluate cognitive complexity of modeling languages, among them, metric based and empirical approaches aremore important and convenient than others. In this paper, we compared these two methods. Results show that there is no significant relation between outputs generated by these approaches.

3

Learning in games with bounded memory

Roy J.

Control and Cybernetics

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2006

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Vol. 35, no 2

303-333

EN

The paper studies infinite repetition of finite strategic form games. Players use a backward looking learning behavior and face bounds on their cognitive capacities. We show that for any given belief-probability over the set of possible outcomes where players have no experience, games can be payoff classified and there always exists a stationary state in the space of action profiles. In particular, if the belief-probability assumes all possible outcomes without experience to be equally likely, in one class of Prisoners' Dilemmas where the uniformly weighted average defecting payoff is higher than the cooperative payoff and the uniformly weighted average cooperative payoff is lower than the defecting payoff, play converges in the long run to the static Nash equilibrium while in the other class of Prisoners' Dilemmas where the reverse holds, play converges to cooperation. Results are applied to a large class of 2 x 2 games.