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Quantitative Assessment of the Modularization of Security Design Patterns with Aspects

Following the success of software engineering design patterns, security patterns are a promising approach to aid in the design and development of more secure software systems. At the same time, recent work on aspect-oriented programming (AOP) suggests that the cross-cutting nature of software security concerns makes it a good candidate for AOP techniques. This work uses a set of software metrics to evaluate and compare object-oriented and aspect-oriented implementations of five security patterns--Secure Base Action, Intercepting Validator, Authentication Enforcer, Authorization Enforcer, and Secure Logger.
Results show that complete separation of concerns was achieved with the aspect-oriented implementations and the modularity of the base application was improved, but at a cost of increased complexity in the security pattern code. In most cases the cohesion, coupling, and size metrics were improved for the base application but worsened for the security pattern package. Furthermore, a partial aspect-oriented solution, where the pattern code is decoupled from the base application but not completely encapsulated by the aspect, demonstrated better modularity and reusability than a full aspect solution.
This study makes several contributions to the fields of aspect-oriented programming and security patterns. It presents quantitative evidence of the effect of aspectization on the modularity of security pattern implementations. It augments four existing security pattern descriptions with aspect-oriented solution strategies, complete with new class and sequence diagrams based on proposed aspect-oriented UML extensions. Finally, it provides a set of role-based refactoring instructions for each security pattern, along with a proposal for three new basic generalization refactorings for aspects.

Identiferoai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1141
Date01 January 2010
CreatorsEdge, Crystal
PublisherNSUWorks
Source SetsNova Southeastern University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceCEC Theses and Dissertations

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