On the Use of Clone Detection for Identifying Crosscutting Concern Code
IEEE Transactions on Software Engineering
Refactoring the Aspectizable Interfaces: An Empirical Assessment
IEEE Transactions on Software Engineering
Timna: a framework for automatically combining aspect mining analyses
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Refactoring a legacy component for reuse in a software product line: a case study: Practice Articles
Journal of Software Maintenance and Evolution: Research and Practice - IEEE International Conference on Software Maintenance (ICSM2005)
Discovering faults in idiom-based exception handling
Proceedings of the 28th international conference on Software engineering
Tool-Supported Refactoring of Existing Object-Oriented Code into Aspects
IEEE Transactions on Software Engineering
Automated Inference of Pointcuts in Aspect-Oriented Refactoring
ICSE '07 Proceedings of the 29th international conference on Software Engineering
PASTE '07 Proceedings of the 7th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Bridging the gap between aspect mining and refactoring
Proceedings of the 3rd workshop on Linking aspect technology and evolution
On some criteria for comparing aspect mining techniques
Proceedings of the 3rd workshop on Linking aspect technology and evolution
Improving modularity by refactoring code clones: a feasibility study on Linux
ACM SIGSOFT Software Engineering Notes
Scalable detection of semantic clones
Proceedings of the 30th international conference on Software engineering
Empirical evaluation of clone detection using syntax suffix trees
Empirical Software Engineering
Journal of Systems and Software
A partitional clustering algorithm for crosscutting concerns identification
SEPADS'09 Proceedings of the 8th WSEAS International Conference on Software engineering, parallel and distributed systems
Automated Aspect Recommendation through Clustering-Based Fan-in Analysis
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Delving source code with formal concept analysis
Computer Languages, Systems and Structures
A survey of automated code-level aspect mining techniques
Transactions on aspect-oriented software development IV
Identifying crosscutting concerns using historical code changes
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Separation of scattered concerns: a graph based approach for aspect mining
ACM SIGSOFT Software Engineering Notes
On the impact of crosscutting concern projection on code measurement
Proceedings of the tenth international conference on Aspect-oriented software development
Aspect recommendation for evolving software
Proceedings of the 33rd International Conference on Software Engineering
Automated pattern-based pointcut generation
SC'06 Proceedings of the 5th international conference on Software Composition
TOSKANA: a toolkit for operating system kernel aspects
Transactions on Aspect-Oriented Software Development II
Construction and analysis of vector space models for use in aspect mining
Proceedings of the 50th Annual Southeast Regional Conference
Empirical Software Engineering
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Code implementing a crosscutting concern is often spread over many different parts of an application. Identifying such code automatically greatly improves both the maintainability and the evolvability of the application. First of all, it allows a developer to more easily find the places in the code that must be changed when the concern changes, and thus makes such changes less time consuming and less prone to errors. Second, it allows a developer to refactor the code, so that it uses modern and more advanced abstraction mechanisms, thereby restoring its modularity. In this paper, we evaluate the suitability of clone detection as a technique for the identification of crosscutting concerns. To that end, we manually identify four specific concerns in an industrial C application, and analyze to what extent clone detection is capable of finding these concerns. We consider our results as a stepping stone toward an automated "concern miner" based on clone detection.