Automatizing Software Cognitive Complexity Reduction

Abstract

Software plays a central role in our life nowadays. We use it almost anywhere, at any time, and for everything: to browse the Internet, to check our emails, and even to access critical services such as health monitoring and banking. Hence, its reliability and general quality is critical. As software increases in complexity, developers spend more time fixing bugs or making code work rather than designing or writing new code. Thus, improving software understandability and maintainability would translate into an economic relief over the total cost of a project. Different cognitive complexity measures have been proposed to quantify the understandability of a piece of code and, therefore, its maintainability. However, the cognitive complexity metric provided by SonarSource and integrated in SonarCloud and SonarQube is quickly spreading in the software industry due to the popularity of these well-known static code tools for evaluating software quality. Despite SonarQube suggests to keep method's cognitive complexity no greater than 15, reducing method's complexity is challenging for a human programmer and there are no approaches to assist developers on this task. 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.

Cognitive Complexity Beyond Generalization: A Subjective Rating for the Human Comprehension

“…Accordingly, the usage of these cognitive weights to denote the cognitive complexity of software cannot be verified. Surprisingly, a metric to demonstrate the cognitive complexity by evaluating the control flow of the source code has been introduced recently in SonarSource software projects [21], which reveals the comprehension dissimilarity along with the same Cyclomatic Complexity values [22]. However, considerable works have not been conducted to evaluate the subjectivity of the human cognition effort to be aligned with the definition of the cognitive complexity, which can be emphasized as the major problem behind the verification of these computations.…”

A Rule-Based Algorithm and Its Specializations for Measuring the Complexity of Software in Educational Digital Environments

“…The intended use of the EduC metric is assessment of source code in educational programming courses, MOOCs, and automated systems for interviewing candidates for employment. EduC adds additional complexity increments with the aim of detecting anti-patterns that make code less maintainable [42], so the metric can be used to analyze and refactor existing software systems either manually or automatically, as is shown in [43].…”