On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software
Riasat Mahbub, Mohammad Masudur Rahman, Muhammad Ahsanul Habib

TL;DR
This study investigates the prevalence, evolution, and impact of code smells in simulation modeling software, revealing certain smells are more common and persist longer, but are not strongly linked to bugs.
Contribution
First empirical analysis of code smell prevalence and evolution in simulation software, highlighting differences from traditional software systems.
Findings
Certain code smells are more prevalent in simulation software.
Some code smells persist across multiple versions.
No significant link between code smells and bugs in simulation systems.
Abstract
Simulation modelling systems are routinely used to test or understand real-world scenarios in a controlled setting. They have found numerous applications in scientific research, engineering, and industrial operations. Due to their complex nature, the simulation systems could suffer from various code quality issues and technical debt. However, to date, there has not been any investigation into their code quality issues (e.g. code smells). In this paper, we conduct an empirical study investigating the prevalence, evolution, and impact of code smells in simulation software systems. First, we employ static analysis tools (e.g. Designite) to detect and quantify the prevalence of various code smells in 155 simulation and 327 traditional projects from Github. Our findings reveal that certain code smells (e.g. Long Statement, Magic Number) are more prevalent in simulation software systems than…
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Taxonomy
TopicsSoftware Engineering Research · Software Testing and Debugging Techniques · Software Reliability and Analysis Research
