Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
A UML-based aspect-oriented design notation for AspectJ
AOSD '02 Proceedings of the 1st international conference on Aspect-oriented software development
Separation of coordination in a dynamic aspect oriented framework
AOSD '02 Proceedings of the 1st international conference on Aspect-oriented software development
Aspect-Oriented Analysis and Design
Aspect-Oriented Analysis and Design
Distribution and persistence as aspects
Software—Practice & Experience - Research Articles
Framework specialization aspects
Proceedings of the 6th international conference on Aspect-oriented software development
A meta-level specification and profile for AspectJ in UML
Proceedings of the 10th international workshop on Aspect-oriented modeling
Towards executable aspect-oriented UML models
Proceedings of the 10th international workshop on Aspect-oriented modeling
A pattern to design crosscutting frameworks
Proceedings of the 2008 ACM symposium on Applied computing
UML-AOF: a profile for modeling aspect-oriented frameworks
Proceedings of the 13th workshop on Aspect-oriented modeling
A process for aspect-oriented platform-specific profile checking
Proceedings of the 2011 international workshop on Early aspects
The crosscutting impact of the AOSD Brazilian research community
Journal of Systems and Software
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In this paper we present UML-AOF and an empirical evaluation about it. UML-AOF is a profile that aims to make the design characteristics of aspect-oriented frameworks (AOFs) more evident in models than standard profiles for aspect-oriented programming (AOP). UML-AOF gathers several stereotypes and tagged values which represent design and architectural details commonly found in AOFs, such as some idioms, patterns and extension mechanisms. In order to evaluate the applicability of this profile, an empirical study was conducted to analyze the number of errors and the time spent by software engineers using both UML-AOF and a conventional profile for AOP. Based on the collected data, we have observed that the number of errors as well the time spent analyzing the models were significantly better than using the conventional profile.