On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software

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 in traditional software systems. Second, we analyze the evolution of these code smells across multiple project versions and investigate their chances of survival. Our experiments show that some code smells such as Magic Number and Long Parameter List can survive a long time in simulation software systems. Finally, we examine any association between software bugs and code smells. Our experiments show that although Design and Architecture code smells are introduced simultaneously with bugs, there is no significant association between code smells and bugs in simulation systems.