On the correlation between Architectural Smells and Static Analysis Warnings (2406.17354v1)

Abstract: Background. Software quality assurance is essential during software development and maintenance. Static Analysis Tools (SAT) are widely used for assessing code quality. Architectural smells are becoming more daunting to address and evaluate among quality issues. Objective. We aim to understand the relationships between static analysis warnings (SAW) and architectural smells (AS) to guide developers/maintainers in focusing their efforts on SAWs more prone to co-occurring with AS. Method. We performed an empirical study on 103 Java projects totaling 72 million LOC belonging to projects from a vast set of domains, and 785 SAW detected by four SAT, Checkstyle, Findbugs, PMD, SonarQube, and 4 architectural smells detected by ARCAN tool. We analyzed how SAWs influence AS presence. Finally, we proposed an AS remediation effort prioritization based on SAW severity and SAW proneness to specific ASs. Results. Our study reveals a moderate correlation between SAWs and ASs. Different combinations of SATs and SAWs significantly affect AS occurrence, with certain SAWs more likely to co-occur with specific ASs. Conversely, 33.79% of SAWs act as "healthy carriers", not associated with any ASs. Conclusion. Practitioners can ignore about a third of SAWs and focus on those most likely to be associated with ASs. Prioritizing AS remediation based on SAW severity or SAW proneness to specific ASs results in effective rankings like those based on AS severity.