NDLI: Construction and analysis of vector space models for use in aspect mining

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Construction and analysis of vector space models for use in aspect mining

Content Provider	ACM Digital Library
Author	Tribbey, William Mitropoulos, Frank
Abstract	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.
Starting Page	220
Ending Page	225
Page Count	6
File Format	PDF
ISBN	9781450312035
DOI	10.1145/2184512.2184564
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-03-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Aspect mining Software evolution Clustering Data mining Refactoring Aspect-oriented software development
Content Type	Text
Resource Type	Article

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