Empirical Validation of Aspect Oriented Metrics
Using Cognitive Complexity and Fuzzy Inference Systems
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To empirically validate the effectiveness of metrics in the context of AOP, three specific metrics are considered:Weighted Method per Class (WMC) - This metric represents the sum of complexities of all methods in a class. A higher WMC value indicates a class with more behavior or logic, which may be harder to understand and maintain.Coupling on Advice Execution (CAE) - This metric quantifies how tightly aspects are coupled with other classes based on advice execution. High coupling can increase maintenance difficulty and reduce modularity.Weighted Pointcut per Aspect (WPA) - This metric counts...
To empirically validate the effectiveness of metrics in the context of AOP, three specific metrics are considered:Weighted Method per Class (WMC) - This metric represents the sum of complexities of all methods in a class. A higher WMC value indicates a class with more behavior or logic, which may be harder to understand and maintain.Coupling on Advice Execution (CAE) - This metric quantifies how tightly aspects are coupled with other classes based on advice execution. High coupling can increase maintenance difficulty and reduce modularity.Weighted Pointcut per Aspect (WPA) - This metric counts and weights the complexity of pointcuts defined in an aspect. A higher WPA suggests an aspect that interacts with many parts of the program, potentially increasing its cognitive load.To enhance the evaluation of these metrics, a Cognitive Weighted Metric is proposed. This novel metric attempts to integrate cognitive complexity considerations into the existing metrics (WMC, CAE, WPA) by assigning weights based on human understanding effort. However, as software complexity is not always binary or linear, traditional crisp logic systems may not suffice to model such intricacies.
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