The weighted combined algorithm: a linkage algorithm for software clustering

Maqbool, Onaiza; Babri, Haroon A.

doi:10.1109/csmr.2004.1281402

Cited by 79 publications

(85 citation statements)

References 16 publications

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“…The complete linkage (CL) clustering algorithm was shown to be generally the most accurate, while ACDC was shown to have limited accuracy. WCA was shown to be superior to CL in yet another study [26]. Finally, Software Architecture Finder (SArF) [23], another technique that maximizes an objective function, has recently been compared against ACDC and Bunch [23].…”

Section: Related Workmentioning

confidence: 99%

A comparative analysis of software architecture recovery techniques

Garcia

Ivkovic

Medvidović

2013

2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)

131

142

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Abstract-Many automated techniques of varying accuracy have been developed to help recover the architecture of a software system from its implementation. However, rigorously assessing these techniques has been hampered by the lack of architectural "ground truths". Over the past several years, we have collected a set of eight architectures that have been recovered from opensource systems and independently, carefully verified. In this paper, we use these architectures as ground truths in performing a comparative analysis of six state-of-the-art software architecture recovery techniques. We use a number of metrics to assess each technique for its ability to identify a system's architectural components and overall architectural structure. Our results suggest that two of the techniques routinely outperform the rest, but even the best of the lot has surprisingly low accuracy. Based on the empirical data, we identify several avenues of future research in software architecture recovery.

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Section: Related Workmentioning

confidence: 99%

A comparative analysis of software architecture recovery techniques

Garcia

Ivkovic

Medvidović

2013

2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)

131

142

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“…Six techniques are selected for this purpose which are: ACDC [6], BUNCH [7], WCA [18], LIMBO [19], ZoneBased Recovery (ZBR) [31] and Architecture Recovery using Concerns (ARC) [32]. These techniques are evaluated in terms of accuracy, authoritativeness and in terms of the recovery criteria used.…”

Section: Related Workmentioning

confidence: 99%

“…In [18], a linkage algorithm, Weighted combined algorithm (WCA) is proposed for software clustering. Comparative analysis is performed between WCA, Complete linkage (CL) and Combined Algorithm (CA) [27].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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“…• Weighted Combined Algorithm (WCA) -A hierarchical clustering algorithm created by Maqbool and Babri [4] which is the most successful hierarchical algorithm. We will use this algorithm for our tests.…”

Section: Definitionsmentioning

confidence: 99%