Munch: An Efficient Modularisation Strategy to Assess the Degree of Refactoring on Sequential Source Code Checkings

Arzoky, Mahir; Swift, Stephen; Tucker, Allan; Cain, James

doi:10.1109/icstw.2011.87

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…This work significantly extends that of Arzoky et al [Arzok11] and follows Mancoridis et al and Mitchell [Manco02] [Mitch02], who first introduced search-based approach to software modularisation. The clustering algorithm was re-implemented from available literature on Bunch's clustering algorithm [Manco98] to form a tool called Munch.…”

Section: Clustering Algorithmsupporting

confidence: 64%

“…We are not treating our dataset as separate modularisation problems, but instead we are using the previous results of modularisation to give us a head start. This paper extends the work of Arzoky et al [Arzok11], which introduced the seeding technique to modularise the time-series dataset. Although a few studies have looked at using the concept of seeding for clustering, for example [Sures10] [Swift04], none have looked at using seeding to modularise sequential source code software versions.…”

Section: Introductionmentioning

confidence: 53%

“…In our previous paper [Arzok11], we experimented with two fitness functions: the Modularisation Quality (MQ) metric of Mancoridis et al [Manco98], as implemented in Bunch; and the EValuation Metric (EVM) of Tucker et al [Tucke01], previously applied to problems of time-series data and the clustering of gene expression data. We also introduced EValuation Metric Difference (EVMD), a faster version of the EVM function.…”

Section: Fitness Functionsmentioning

confidence: 99%

“…We also introduced EValuation Metric Difference (EVMD), a faster version of the EVM function. EVMD was selected as the fitness function for the modularisations as it is more robust than MQ and faster than EVM [Arzok11].…”

Section: Fitness Functionsmentioning

confidence: 99%

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A Seeded Search for the Modularisation of Sequential Software Versions.

Arzoky¹,

Swift²,

Tucker³

et al. 2012

JOT

Self Cite

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Software module clustering is the process of partitioning the structure of the software system using low-level dependencies in the source code in order to understand and improve the system's structure. A software clustering tool, Munch, was used to modularise sequential source code software check-ins to assess the degree of major changes. It uses a searchbased software engineering technique. This paper employs a seeding technique, based on results from previous modularisations, to speed up the process and reduce the running time. In order to evaluate the efficiency of the modularisation we conducted a number of experiments on our uniquely large and comprehensive real-world dataset. The results of the experiments present strong evidence to support the seeding strategy.

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Section: Clustering Algorithmsupporting

confidence: 64%

Section: Introductionmentioning

confidence: 53%

Section: Fitness Functionsmentioning

confidence: 99%

Section: Fitness Functionsmentioning

confidence: 99%

See 2 more Smart Citations

A Seeded Search for the Modularisation of Sequential Software Versions.

Arzoky¹,

Swift²,

Tucker³

et al. 2012

JOT

Self Cite

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“…TurboMQ supports weighted graphs, and has much lower computational complexity than Basic MQ. Turbo MQ has been used to evaluate many of the clustering techniques published in the literature including [9], [35] and [46].…”

Section: Quality Of Partitioningmentioning

confidence: 99%

Cooperative based software clustering on dependency graphs

Ibrahim

Rayside

Kashef

2014

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

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The organization of software systems into subsystems is usually based on the constructs of packages or modules and has a major impact on the maintainability of the software. However, during software evolution, the organization of the system is subject to continual modication, which can cause it to drift away from the original design, often with the eect of reducing its quality.A number of techniques for evaluating a system's maintainability and for controlling the eort required to conduct maintenance activities involve software clustering. Software clustering refers to the partitioning of software system components into clusters in order to obtain both exterior and interior connectivity between these components. It helps maintainers enhance the quality of software modularization and improve its maintainability.

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