Software fault prediction for object oriented systems: a literature review
ACM SIGSOFT Software Engineering Notes
On the relationship of concern metrics and requirements maintainability
Information and Software Technology
On the role of composition code properties on evolving programs
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Usage and testability of AOP: An empirical study of AspectJ
Information and Software Technology
The crosscutting impact of the AOSD Brazilian research community
Journal of Systems and Software
Enhancing design models with composition properties: a software maintenance study
Proceedings of the 12th annual international conference on Aspect-oriented software development
Linking Cyclicality and Product Quality
Manufacturing & Service Operations Management
Hi-index | 0.00 |
Coupling in software applications is often used as an indicator of external quality attributes such as fault-proneness. In fact, the correlation of coupling metrics and faults in object oriented programs has been widely studied. However, there is very limited knowledge about which coupling properties in aspect-oriented programming (AOP) are effective indicators of faults in modules. Existing coupling metrics do not take into account the specificities of AOP mechanisms. As a result, these metrics are unlikely to provide optimal predictions of pivotal quality attributes such as fault-proneness. This impacts further by restraining the assessments of AOP empirical studies. To address these issues, this paper presents an empirical study to evaluate the impact of coupling sourced from AOP-specific mechanisms. We utilise a novel set of coupling metrics to predict fault occurrences in aspect-oriented programs. We also compare these new metrics against previously proposed metrics for AOP. More specifically, we analyse faults from several releases of three AspectJ applications and perform statistical analyses to reveal the effectiveness of these metrics when predicting faults. Our study shows that a particular set of fine-grained directed coupling metrics have the potential to help create better fault prediction models for AO programs.