1,012 research outputs found
A common framework for aspect mining based on crosscutting concern sorts
The increasing number of aspect mining techniques proposed in literature calls for a methodological way of comparing and combining them in order to assess, and improve on, their quality. This paper addresses this situation by proposing a common framework based on crosscutting concern sorts which allows for consistent assessment, comparison and combination of aspect mining techniques. The framework identifies a set of requirements that ensure homogeneity in formulating the mining goals, presenting the results and assessing their quality.
We demonstrate feasibility of the approach by retrofitting an existing aspect mining technique to the framework, and by using it to design and implement two new mining techniques. We apply the three techniques to a known aspect mining benchmark and show how they can be consistently assessed
and combined to increase the quality of the results. The techniques and combinations are implemented in FINT, our publicly available free aspect mining tool
A common framework for aspect mining based on crosscutting concern sorts
The increasing number of aspect mining techniques proposed in literature calls for a methodological way of comparing and combining them in order to assess, and improve on, their quality. This paper addresses this situation by proposing a common framework based on crosscutting concern sorts which allows for consistent assessment, comparison and combination of aspect mining techniques. The framework identifies a set of requirements that ensure homogeneity in formulating the mining goals, presenting the results and assessing their quality. We demonstrate feasibility of the approach by retrofitting an existing aspect mining technique to the framework, and by using it to design and implement two new mining techniques. We apply the three techniques to a known aspect mining benchmark and show how they can be consistently assessed and combined to increase the quality of the results. The techniques and combinations are implemented in FINT, our publicly available free aspect mining tool
06302 Abstracts Collection -- Aspects For Legacy Applications
From 26.07.06 to 29.07.06, the Dagstuhl Seminar 06302 ``Aspects For Legacy Applications\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Identifying Crosscutting Concerns Using Fan-in Analysis
Aspect mining is a reverse engineering process that aims at finding
crosscutting concerns in existing systems. This paper proposes an aspect mining
approach based on determining methods that are called from many different
places, and hence have a high fan-in, which can be seen as a symptom of
crosscutting functionality. The approach is semi-automatic, and consists of
three steps: metric calculation, method filtering, and call site analysis.
Carrying out these steps is an interactive process supported by an Eclipse
plug-in called FINT. Fan-in analysis has been applied to three open source Java
systems, totaling around 200,000 lines of code. The most interesting concerns
identified are discussed in detail, which includes several concerns not
previously discussed in the aspect-oriented literature. The results show that a
significant number of crosscutting concerns can be recognized using fan-in
analysis, and each of the three steps can be supported by tools.Comment: 34+4 pages; Extended version [Marin et al. 2004a
Applying and Combining Three Different Aspect Mining Techniques
Understanding a software system at source-code level requires understanding
the different concerns that it addresses, which in turn requires a way to
identify these concerns in the source code. Whereas some concerns are
explicitly represented by program entities (like classes, methods and
variables) and thus are easy to identify, crosscutting concerns are not
captured by a single program entity but are scattered over many program
entities and are tangled with the other concerns. Because of their crosscutting
nature, such crosscutting concerns are difficult to identify, and reduce the
understandability of the system as a whole.
In this paper, we report on a combined experiment in which we try to identify
crosscutting concerns in the JHotDraw framework automatically. We first apply
three independently developed aspect mining techniques to JHotDraw and evaluate
and compare their results. Based on this analysis, we present three interesting
combinations of these three techniques, and show how these combinations provide
a more complete coverage of the detected concerns as compared to the original
techniques individually. Our results are a first step towards improving the
understandability of a system that contains crosscutting concerns, and can be
used as a basis for refactoring the identified crosscutting concerns into
aspects.Comment: 28 page
A Hierarchical Clustering Based Approach in Aspect Mining
A Hierarchical Clustering Based Approach in Aspect Mining Clustering is a division of data into groups of similar objects. Aspect mining is a process that tries to identify crosscutting concerns in existing software systems. The goal is to refactor the existing systems to use aspect oriented programming, in order to make them easier to maintain and to evolve. The aim of this paper is to present a new hierarchical clustering based approach in aspect mining. For this purpose we propose HAC algorithm (Hierarchical Agglomerative Clustering in aspect mining). Clustering is used in order to identify crosscutting concerns. We evaluate the obtained results from the aspect mining point of view, based on two quality measures that we have previously introduced and a newly defined one. The proposed approach is compared with other similar existing approaches in aspect mining and two case studies are also reported
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