Weighted Class Complexity: A Measure of Complexity for Object Oriented System

Software complexity metrics are used to predict critical information about reliability and maintainability of software systems. Object oriented software development requires a different approach to software complexity metrics. In this paper, we propose a metric to compute the structural and cognitive complexity of class by associating a weight to the class, called as Weighted Class Complexity (WCC). On the contrary, of the other metrics used for object oriented systems, proposed metric calculates the complexity of a class due to methods and attributes in terms of cognitive weight. The proposed metric has been demonstrated with OO examples. The theoretical and practical evaluations based on the information theory have shown that the proposed metric is on ratio scale and satisfies most of the parameters required by the measurement theor

An Approach for the Empirical Validation of Software Complexity Measures

Software metrics are widely accepted tools to control and assure software quality. A large number of software metrics with a variety of content can be found in the literature; however most of them are not adopted in industry as they are seen as irrelevant to needs, as they are unsupported, and the major reason behind this is due to improper empirical validation. This paper tries to identify possible root causes for the improper empirical validation of the software metrics. A practical model for the empirical validation of software metrics is proposed along with root causes. The model is validated by applying it to recently proposed and well known metrics

The GIST of Concepts

A unified general theory of human concept learning based on the idea that humans detect invariance patterns in categorical stimuli as a necessary precursor to concept formation is proposed and tested. In GIST (generalized invariance structure theory) invariants are detected via a perturbation mechanism of dimension suppression referred to as dimensional binding. Structural information acquired by this process is stored as a compound memory trace termed an ideotype. Ideotypes inform the subsystems that are responsible for learnability judgments, rule formation, and other types of concept representations. We show that GIST is more general (e.g., it works on continuous, semi-continuous, and binary stimuli) and makes much more accurate predictions than the leading models of concept learning difficulty,such as those based on a complexity reduction principle (e.g., number of mental models,structural invariance, algebraic complexity, and minimal description length) and those based on selective attention and similarity (GCM, ALCOVE, and SUSTAIN). GIST unifies these two key aspects of concept learning and categorization. Empirical evidence from three\ud experiments corroborates the predictions made by the theory and its core model which we propose as a candidate law of human conceptual behavior

A Suite of Object Oriented Cognitive Complexity Metrics

Object orientation has gained a wide adoption in the software development community. To this end, different metrics that can be utilized in measuring and improving the quality of object-oriented (OO) software have been proposed, by providing insight into the maintainability and reliability of the system. Some of these software metrics are based on cognitive weight and are referred to as cognitive complexity metrics. It is our objective in this paper to present a suite of cognitive complexity metrics that can be used to evaluate OO software projects. The present suite of metrics includes method complexity, message complexity, attribute complexity, weighted class complexity, and code complexity. The metrics suite was evaluated theoretically using measurement theory and Weyuker’s properties, practically using Kaner’s framework and empirically using thirty projects

WEAK MEASUREMENT THEORY AND MODIFIED COGNITIVE COMPLEXITY MEASURE

Measurement is one of the problems in the area of software engineering. Since traditional measurement theory has a major problem in defining empirical observations on software entities in terms of their measured quantities, Morasca has tried to solve this problem by proposing Weak Measurement theory. In this paper, we tried to evaluate the applicability of weak measurement theory by applying it on a newly proposed Modified Cognitive Complexity Measure (MCCM). We also investigated the applicability of Weak Extensive Structure for deciding on the type of scale for MCCM. It is observed that the MCCM is on weak ratio scale

Tool Support for Cascading Style Sheets’ Complexity Metrics

Tools are the fundamental requirement for acceptability of any metrics programme in the software industry. It is observed that majority of the metrics proposed and are available in the literature lack tool support. This is one of the reasons why they are not widely accepted by the practitioners. In order to improve the acceptability of proposed metrics among software engineers that develop Web applications, there is need to automate the process. In this paper, we have developed a tool for computing metrics for Cascading Style Sheets (CSS) and named it as CSS Analyzer (CSSA). The tool is capable of measuring different metrics, which are the representation of different quality attributes: which include understandability, reliability and maintainability based on some previously proposed metrics. The tool was evaluated by comparing its result on 40 cascading style sheets with results gotten by the manual process of computing the complexities. The results show that the tool computes in far less time when compared to the manual process and is 51.25% accurate

Assessing Cognitive Complexity in Java-Based Object-Oriented Systems: Metrics and Tool Support

Software cognitive complexity refers to how demanding the mental process of performing tasks such as coding, testing, debugging, or modifying source code is. Achieving low levels of cognitive complexity is crucial for ensuring high levels of software maintainability, which is one of the most rewardful software quality attributes. Therefore, in order to control and ensure software maintainability, it is first necessary to accurately quantify software cognitive complexity. In this line, this paper presents a software metric to assess cognitive complexity in Object-Oriented (OO) systems, and particularly those developed in the Java language, which is very popular among OO programming languages. The proposed metric is based on a characterization of basic control structures present in Java systems. Several algorithms to compute the metric and their materialization in the Eclipse IDE are also introduced. Finally, a theoretical validation of the metric against a framework specially designed to validate software complexity metrics is presented, and the applicability of the tool is shown by illustrating the metric in the context of ten real world Java projects and relevant metrics from the well-known Chidamber-Kemerer metric suite

Evaluation Criteria for Object-oriented Metrics

In this paper an evaluation model for object-oriented (OO) metrics is proposed. We have evaluated the existing evaluation criteria for OO metrics, and based on the observations, a model is proposed which tries to cover most of the features for the evaluation of OO metrics. The model is validated by applying it to existing OO metrics. In contrast to the other existing criteria, the proposed model is simple in implementation and includes the practical and important aspects of evaluation; hence it suitable to evaluate and validate any OO complexity metric