Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Mining Aspectual Views using Formal Concept Analysis
SCAM '04 Proceedings of the Source Code Analysis and Manipulation, Fourth IEEE International Workshop
Aspect Mining Using Event Traces
Proceedings of the 19th IEEE international conference on Automated software engineering
Using language clues to discover crosscutting concerns
MACS '05 Proceedings of the 2005 workshop on Modeling and analysis of concerns in software
On the Use of Clone Detection for Identifying Crosscutting Concern Code
IEEE Transactions on Software Engineering
A common framework for aspect mining based on crosscutting concern sorts
WCRE '06 Proceedings of the 13th Working Conference on Reverse Engineering
Aspect Mining Using Method Call Tree
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Applying Systematic Reviews to Diverse Study Types: An Experience Report
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
A Visual Text Mining approach for Systematic Reviews
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Identifying Crosscutting Concerns Using Fan-In Analysis
ACM Transactions on Software Engineering and Methodology (TOSEM)
ICPC '08 Proceedings of the 2008 The 16th IEEE International Conference on Program Comprehension
Do Crosscutting Concerns Cause Defects?
IEEE Transactions on Software Engineering
Mining Coding Patterns to Detect Crosscutting Concerns in Java Programs
WCRE '08 Proceedings of the 2008 15th Working Conference on Reverse Engineering
Systematic literature reviews in software engineering - A systematic literature review
Information and Software Technology
Using Dataflow Information for Concern Identification in Object-Oriented Software Systems
CSMR '08 Proceedings of the 2008 12th European Conference on Software Maintenance and Reengineering
Automatic Support for the Migration Towards Aspects
CSMR '08 Proceedings of the 2008 12th European Conference on Software Maintenance and Reengineering
Aspect mining using self-organizing maps with method level dynamic software metrics as input vectors
Aspect mining using self-organizing maps with method level dynamic software metrics as input vectors
Automated Aspect Recommendation through Clustering-Based Fan-in Analysis
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
WCRE '09 Proceedings of the 2009 16th Working Conference on Reverse Engineering
A survey of automated code-level aspect mining techniques
Transactions on aspect-oriented software development IV
Identifying cross-cutting concerns using software repository mining
Proceedings of the Joint ERCIM Workshop on Software Evolution (EVOL) and International Workshop on Principles of Software Evolution (IWPSE)
Aspect Mining Using Link Analysis
FCST '10 Proceedings of the 2010 Fifth International Conference on Frontier of Computer Science and Technology
Aspect recommendation for evolving software
Proceedings of the 33rd International Conference on Software Engineering
Mining Crosscutting Concerns through Random Walks
IEEE Transactions on Software Engineering
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Background: The several maintenance tasks a system is submitted during its life usually cause its architecture deviates from the original conceivable design, ending up with scattered and tangled concerns across the software. The research area named concern mining attempts to identify such scattered and tangled concerns to support maintenance and reverse-engineering. Objectives: The aim of this paper is threefold: (i) identifying techniques employed in this research area, (ii) extending a taxonomy available on the literature and (iii) recommending an initial combination of some techniques. Results: We selected 62 papers by their mining technique. Among these papers, we identified 18 mining techniques for crosscutting concern. Based on these techniques, we have extended a taxonomy available in the literature, which can be used to position each new technique, and to compare it with the existing ones along relevant dimensions. As consequence, we present some combinations of these techniques taking into account high values of precision and recall that could improve the identification of both Persistence and Observer concerns. The combination that we recommend may serve as a roadmap to potential users of mining techniques for crosscutting concerns.