Examining the Relationship of Code and Architectural Smells with Software Vulnerabilities

TL;DR

This study investigates the correlation between code and architectural smells and software vulnerabilities, finding certain code smells significantly associated with vulnerabilities across multiple systems, which can aid in vulnerability prediction.

Contribution

It identifies specific code smells that are statistically linked to vulnerabilities, providing insights for improving vulnerability prediction models.

Findings

Certain code smells are significantly associated with vulnerabilities.

No significant relationship found between architectural smells and vulnerabilities.

Vulnerable classes tend to have specific code smells more frequently.

Abstract

Context: Security is vital to software developed for commercial or personal use. Although more organizations are realizing the importance of applying secure coding practices, in many of them, security concerns are not known or addressed until a security failure occurs. The root cause of security failures is vulnerable code. While metrics have been used to predict software vulnerabilities, we explore the relationship between code and architectural smells with security weaknesses. As smells are surface indicators of a deeper problem in software, determining the relationship between smells and software vulnerabilities can play a significant role in vulnerability prediction models. Objective: This study explores the relationship between smells and software vulnerabilities to identify the smells. Method: We extracted the class, method, file, and package level smells for three systems: Apache Tomcat, Apache CXF, and Android. We then compared their occurrences in the vulnerable classes which were reported to contain vulnerable code and in the neutral classes (non-vulnerable classes where no vulnerability had yet been reported). Results: We found that a vulnerable class is more likely to have certain smells compared to a non-vulnerable class. God Class, Complex Class, Large Class, Data Class, Feature Envy, Brain Class have a statistically significant relationship with software vulnerabilities. We found no significant relationship between architectural smells and software vulnerabilities. Conclusion: We can conclude that for all the systems examined, there is a statistically significant correlation between software vulnerabilities and some smells.