Identifying, Assigning, and Quantifying Crosscutting Concerns

Abstract

Crosscutting concerns degrade software quality. Before we can modularize the crosscutting concerns in our programs to increase software quality, we must first be able to find them. Unfortunately, accurately locating the code related to a concern is difficult, and without proper metrics, determining how much the concern is crosscutting is impossible. We propose a systematic methodology for identifying which code is related to which concern, and a suite of metrics for quantifying the amount of crosscutting code. Our concern identification and assignment guidelines resolve some of the ambiguity issues encountered by other researchers. We applied this approach to systematically identify all the requirement concerns in a 13,531 line program. We found that 95% of the concerns were crosscutting-indicating a significant potential for improving modularity-and that our metrics were better able to determine which concerns would benefit the most from reengineering.

Modeling scenario variability as crosscutting mechanisms

“…These metrics inherit the same properties of the original ones [12]: (a) the degree of scattering (DoS) is normalized between 0 (completely localized) and 1 (completely unlocalized); and (b) the degree of focus (DoF) is also normalized between 0 (completely unfocused) and 1 (completely focused).…”

“…In order to evaluate our approach, we customized the metric suite proposed by Eaddy et al [12], considering the degree of scattering of features and the degree of focus of scenarios. We also customized their prune dependency analysis, as a guide to assign features to scenario steps.…”

“…In fact, this limitation hinders the comparison of modularity between different specifications. As a consequence, we adopted a suite of metrics for quantifying degree of scattering and degree of focus [12].…”

Leveraging Early Aspects in End-to-End Model Driven Development for Non-Functional Properties in Service Oriented Architecture

“…As a result, DOS remains low. In a UML model generated by Ark.bpmn, DOS is consistently higher with all Eaddy et al (2007) claim that DOC is 0.5 when crosscutting concerns are well modularized. DOS is close enough to 0.5 when using BALLAD; BALLAD better modularizes non-functional properties than UML models with UP-SNFPs.…”

“…In order to evaluate how BALLAD separates functional and non-functional properties effectively, this paper uses two metrics: Concern Diffusion over Components (CDC) (Sant'Anna, Garcia, Chavez, Lucena, & Staa, 2003) and Degree of Scattering (DOS) (Eaddy, Aho, & Murphy, 2007). Both are measured for a BPMN model with a BALLAD aspect and a UML model generated by Ark.bpmn.…”