Modularizing Legacy System through an Improved Bunch Clustering Method in Cloud Migration

Zhao, Junfeng; Zhou, Jiantao; Yang, Hongji

doi:10.14257/ijgdc.2015.8.4.01

Cited by 2 publications

(6 citation statements)

References 24 publications

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“…However, the module dependency graph (MDG) on which the Bunch approach relies does not distinguish different dependencies. Taking the above problems into consideration, [3] improved the Bunch approach and adopted R-MDG, which can reflect different dependencies, as the input to make the results more practical.…”

Section: Related Workmentioning

confidence: 99%

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Approach to Discovering Reusable Components in Java Legacy System

Zhao¹,

Li²

2019

IJPE

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With the development of modern society, human beings are more and more aware of the importance of creativity. As a new area of research, creative computing will play a bigger role in life. There is no doubt that a large pool of knowledge is the foundation of creativity. Research shows that legacy systems contain not only a great deal of domain expertise but also many reusable components. From this point, a new component extraction approach is proposed to extract reusable components of the system and provide better foundation and inspiration for creativity. Experiments have shown that this approach can extract reusable components efficiently, which is helpful for creativity.

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Section: Related Workmentioning

confidence: 99%

“…If the system uses a common development framework, we will use the method presented in this article for component extraction. Otherwise, we will use the improved Bunch clustering algorithm for component extraction [3]. This paper is divided into five parts.…”

Section: Introductionmentioning

confidence: 99%

Approach to Discovering Reusable Components in Java Legacy System

Zhao¹,

Li²

2019

IJPE

Self Cite

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“…The technique is implemented in the BUNCH clustering tool [7]. In [11], an improved BUNCH clustering is proposed for modularization of object oriented systems. It is shown that improved BUNCH clustering is able to produce clusters which are more stable with respect to benchmarks.…”

Section: Related Workmentioning

confidence: 99%

“…Incremental clustering is proposed in an attempt to accommodate newly introduced resources (orphan resources) in their appropriate subsystems and corrective clustering is proposed to accommodate structural changes (resource re-adoption). The orphan adoption approach has been adopted in many well-known reverse engineering tools [6,7,8,9,11].…”

Section: Related Workmentioning

confidence: 99%

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Supervised Learning For Orphan Adoption Problem In Software Architecture Recovery

Bibi

Maqbool

Kanwal

2016

MJCS

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Maintenance of architectural documentation is a prime requirement for evolving software systems. New versions of software systems are launched after making the changes that take place in a software system over time. The orphan adoption problem, which deals with the issue of accommodation of newly introduced resources (orphan resources) in appropriate subsystems in successive versions of a software system, is a significant problem. The orphan adoption algorithm has been developed to address this problem. For evolving software systems, it would be useful to recover the architecture of subsequent versions of a software system by using existing architectural information. In this paper, we explore supervised learning techniques (classifiers) for recovering the architecture of subsequent versions of a software system by taking benefit of existing architectural information. We use three classifiers, i.e., Bayesian classifier, k-Nearest Neighbor classifier and Neural Network for orphan adoption. We conduct experiments to compare the performance of the classifiers using various dependencies between entities in a software system. Our experiments highlight correspondence between the orphan adoption algorithm and the classifiers, and also reveal their strengths and weaknesses. To combine strengths of individual classifiers, we propose using a multiclassifier approach in which classifiers work cooperatively to improve classification accuracy. Experiments show that there is significant improvement in results when our proposed multiclassifier approach is used.

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Modularizing Legacy System through an Improved Bunch Clustering Method in Cloud Migration

Abstract: Abstract

Cited by 2 publications

References 24 publications

Approach to Discovering Reusable Components in Java Legacy System

Approach to Discovering Reusable Components in Java Legacy System

Supervised Learning For Orphan Adoption Problem In Software Architecture Recovery

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