ACM Computing Surveys (CSUR) - Special issue: position statements on strategic directions in computing research
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics
IEEE Transactions on Software Engineering
Clustering validity checking methods: part II
ACM SIGMOD Record
Quality Scheme Assessment in the Clustering Process
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
On finding duplication and near-duplication in large software systems
WCRE '95 Proceedings of the Second Working Conference on Reverse Engineering
Clone Detection Using Abstract Syntax Trees
ICSM '98 Proceedings of the International Conference on Software Maintenance
A Language Independent Approach for Detecting Duplicated Code
ICSM '99 Proceedings of the IEEE International Conference on Software Maintenance
AspectJ in Action: Practical Aspect-Oriented Programming
AspectJ in Action: Practical Aspect-Oriented Programming
An Evaluation of Clone Detection Techniques for Identifying Crosscutting Concerns
ICSM '04 Proceedings of the 20th IEEE International Conference on Software Maintenance
Aspect Mining Using Event Traces
Proceedings of the 19th IEEE international conference on Automated software engineering
Aspect Mining through the Formal Concept Analysis of Execution Traces
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
Identifying Aspects Using Fan-In Analysis
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
A New k-means Based Clustering Algorithm in Aspect Mining
SYNASC '06 Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Identifying Crosscutting Concerns Using Fan-In Analysis
ACM Transactions on Software Engineering and Methodology (TOSEM)
Introduction to Information Retrieval
Introduction to Information Retrieval
Do Crosscutting Concerns Cause Defects?
IEEE Transactions on Software Engineering
WCRE '08 Proceedings of the 2008 15th Working Conference on Reverse Engineering
A partitional clustering algorithm for crosscutting concerns identification
SEPADS'09 Proceedings of the 8th WSEAS International Conference on Software engineering, parallel and distributed systems
Aspect mining using model-based clustering
Aspect mining using model-based clustering
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A software system is designed so that its concerns are as independent as possible. Concerns upon which other concerns depend are called crosscutting concerns, examples of which are logging, authentication, and session management. Crosscutting concerns in a software system have the potential to increase the number of defects over time as the system is evolved. Aspect-oriented programming provides an additional layer of abstraction to the object-oriented programming paradigm for the purpose of separating concerns. The search for crosscutting concerns is referred to as aspect mining. Previous aspect mining algorithms used aggregated metric values as components in the vector space model. In this paper a new method for constructing vector space models is proposed that attempts to retain the detail present in the relationships between the elements of a software application. This is done through the use of pattern matrices derived from the non-aggregated metrics. The non-aggregated vector space models are then used in a clustering-based aspect mining algorithm and their performance is evaluated. The results show that this new approach to constructing vector space models is a viable one but needs further investigation. Issues with current measures for evaluating clustering-based aspect mining algorithms are highlighted and directions for further research are given.