The Relationship Between Cognitive Complexity and the Probability of Defects

Alqadi, Basma S.

doi:10.1109/icsme.2019.00095

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…This study validated a cognitive complexity metric as a noteworthy measure of version to version source code change. Also in 2019, Alqadi proposed novel metrics to compute the cognitive complexity of code slices [36]. Empirical investigation into how cognitive complexity correlates with defects in the version histories of three open-source systems was performed.…”

Section: Related Workmentioning

confidence: 99%

Automatizing Software Cognitive Complexity Reduction

et al. 2022

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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.

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Section: Related Workmentioning

confidence: 99%