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 empirical evaluation of the “cognitive complexity” measure as a predictor of code understandability

Background: Code that is difficult to understand is also difficult to inspect and maintain and ultimately causes increased costs. Therefore, it would be greatly beneficial to have source code measures that are related to code understandability. Many ‘‘traditional’’ source code measures, including for instance Lines of Code and McCabe’s Cyclomatic Complexity, have been used to identify hard-to-understand code. In addition, the ‘‘Cognitive Complexity’’ measure was introduced in 2018 with the specific goal of improving the ability to evaluate code understandability. Aims: The goals of this paper are to assess whether (1) ‘‘Cognitive Complexity’’ is better correlated with code understandability than traditional measures, and (2) the availability of the ‘‘Cognitive Complexity’’ measure improves the performance (i.e., the accuracy) of code understandability prediction models. Method: We carried out an empirical study, in which we reused code understandability measures used in several previous studies. We first built Support Vector Regression models of understandability vs. code measures, and we then compared the performance of models that use ‘‘Cognitive Complexity’’ against the performance of models that do not. Results: ‘‘Cognitive Complexity’’ appears to be correlated to code understandability approximately as much as traditional measures, and the performance of models that use ‘‘Cognitive Complexity’’ is extremely close to the performance of models that use only traditional measures. Conclusions: The ‘‘Cognitive Complexity’’ measure does not appear to fulfill the promise of being a significant improvement over previously proposed measures, as far as code understandability prediction is concerned

An Extended Study of the Correlation of Cognitive Complexity-related Code Measures

Several measures have been proposed to represent various characteristics of code, such as size, complexity, cohesion, coupling, etc. These measures are deemed interesting because the internal characteristics they measure (which are not interesting per se) are believed to be correlated with external software qualities (like reliability, maintainability, etc.) that are definitely interesting for developers or users. Although many measures have been proposed for software code, new measures are continuously proposed. However, before starting using a new measure, we would like to ascertain that it is actually useful and that it provides some improvement with respect to well established measures that have been in use for a long time and whose merits have been widely evaluated. In 2018, a new code measure, named “Cognitive Complexity” was proposed. According to the proposers, this measure should correlate to code understandability much better than traditional code measures, such as McCabe Complexity, for instance. However, hardly any experimentation proved whether the “Cognitive Complexity” measure is better than other measures or not. Actually, it was not even verified whether the new measure provides different knowledge concerning code with respect to traditional measures. In this paper, we aim at evaluating experimentally to what extent the new measure is correlated with traditional measures. To this end, we measured the code from a set of open-source Java projects and derived models of “Cognitive Complexity” based on the traditional code measures yielded by a state-of-the-art code measurement tool. We found that fairly accurate models of “Cognitive Complexity” can be obtained using just a few traditional code measures. In this sense, the “Cognitive Complexity” measure does not appear to provide additional knowledge with respect to previously proposed measures

Automatizing Software Cognitive Complexity Reduction

We model the cognitive complexity reduction of a method as an optimization problem where the search space contains all sequences of Extract Method refactoring opportunities. We then propose a novel approach that searches for feasible code extractions allowing developers to apply them, all in an automated way. This will allow software developers to make informed decisions while reducing the complexity of their code. We evaluated our approach over 10 open-source software projects and was able to fix 78% of the 1,050 existing cognitive complexity issues reported by SonarQube. We finally discuss the limitations of the proposed approach and provide interesting findings and guidelines for developers.Universidad de Málaga (grants B1-2020_01 and B4-2019-05) Project PID2020-116727RB-I00 funded by MCIN/AEI /10.13039/501100011033 Rubén Saborido was recipient of a Juan de la Cierva grant FJC2018-038537-I funded by MCIN/AEI /10.13039/501100011033. Javier Ferrer was supported by a postdoc grant (DOC/00488) funded by the Andalusian Ministry of Economic Transformation, Industry, Knowledge and Universities

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