Examining the relationship of code and architectural smells with software vulnerabilities

Kazi Zakia Sultana, Zadia Codabux, Byron Williams

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2020 27th Asia-Pacific Software Engineering Conference, APSEC 2020
PublisherIEEE Computer Society
Pages31-40
Number of pages10
ISBN (Electronic)9781728195537
DOIs
StatePublished - Dec 2020
Event27th Asia-Pacific Software Engineering Conference, APSEC 2020 - Singapore, Singapore
Duration: 1 Dec 20204 Dec 2020

Publication series

NameProceedings - Asia-Pacific Software Engineering Conference, APSEC
Volume2020-December
ISSN (Print)1530-1362

Conference

Conference27th Asia-Pacific Software Engineering Conference, APSEC 2020
CountrySingapore
CitySingapore
Period1/12/204/12/20

Keywords

  • Architectural Smell
  • Code Smell
  • Software Security
  • Vulnerability

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