A New k-means Based Clustering Algorithm in Aspect Mining

Șerban, Gheorghe; Moldovan, Grigoreta Sofia

doi:10.1109/synasc.2006.5

Cited by 16 publications

(16 citation statements)

References 4 publications

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“…This application has become a commonly accepted benchmarking program for aspect mining algorithms [9,25,23]. Although the programming language used here is Java, the vector space models and the mining algorithm to be described can be generally adapted to other languages.…”

Section: Data Preparationmentioning

confidence: 99%

“…As done in [9,25], a partitional clustering algorithm will be used to segment the methods as part of the mining algorithm. The k-means clustering algorithm [16] as implemented by the procedure kmeans in the statistical programming language R [27] was used for this experiment.…”

Section: Clustering Algorithmmentioning

confidence: 99%

“…The attractiveness of the k-means++ seeding is that it offers in the mean an upper bound on the error between the cluster configuration that results from using the k-means++ seeding and the optimal cluster configuration. The initial seeding described in [9] is done in such a way to choose cluster centers that exceed a selected distance threshold. The seeding also determines the number of clusters, and for the same distance threshold produces the same initial clusters.…”

Section: Clustering Algorithmmentioning

confidence: 99%

“…The aspect mining algorithms of interest in this paper are those that involve clustering in which the source code is represented by a vector space model built from quantitative metrics. In particular, the work of Moldovan andŞerban [9,25] and that of Marin, Deursen, and Moonen [23] proposes to use what has become a commonly accepted measure of code scatter, the Fan-In Value (FIV) of a module, to identify aspect candidates. The FIV of a module is defined to be the number of other modules that use or call it.…”

Section: Introductionmentioning

confidence: 99%

“…Note that the FIV is a measure of code scatter and not code tangling. The clustering algorithm in [9,25] makes use of the FIV as part of the vector space model.…”

Section: Introductionmentioning

confidence: 99%

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

Tribbey

Mitropoulos

2012

Proceedings of the 50th Annual Southeast Regional Conference

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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.

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Section: Data Preparationmentioning

confidence: 99%

Section: Clustering Algorithmmentioning

confidence: 99%

Section: Clustering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Note that the FIV is a measure of code scatter and not code tangling. The clustering algorithm in [9,25] makes use of the FIV as part of the vector space model.…”

Section: Introductionmentioning

confidence: 99%

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

Tribbey

Mitropoulos

2012

Proceedings of the 50th Annual Southeast Regional Conference

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Mining static and dynamic crosscutting concerns: a role‐based approach

Bernardi

Cimitile

Lucca

2016

J Software Evolu Process

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The implementation of crosscutting concerns in object-oriented (OO) systems entails scattering and tangling of code across several components increasing code duplication and making the system harder to comprehend, maintain, evolve and reuse. Therefore, identification of crosscutting concerns drives the reengineering or refactoring tasks in order to improve modularization of an existing system and increasing its overall internal quality. This paper proposes an approach to identify and analyse the components implementing the static and dynamic crosscutting in OO systems. The approach defines a meta-model representing the structure of an OO system in terms of its components. A static analysis of an OO software system is performed to create an instance of this meta-model. Such meta-model is finally analysed to find static and dynamic crosscutting among concerns. The effectiveness and efficiency of the proposed approach have been validated in an empirical assessment where it was applied to some OO java systems. The obtained results show a good level of effectiveness for the crosscutting analysis.

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