Using version information in architectural clustering - a case study

Wierda, Andreas; Dortmans, Eric; Somers, Lou

doi:10.1109/csmr.2006.56

Cited by 9 publications

(15 citation statements)

References 34 publications

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“…They show with a case study on a system consisting of around 2,700 classes that cluster analysis was applicable in practice for the given problem size using a hill-climbing algorithm combined with simulated annealing [21].…”

Section: Related Workmentioning

confidence: 99%

“…For our experiments, we decided to use the search-based algorithm as implemented in the Bunch tool [10,20], which has been specifically designed for remodularization purposes, and which has been used in several other remodularization cases [21,20]. Furthermore, performance has been a key driver in the design of Bunch, and Mitchell and Mancoridis claim that relatively large systems can be clustered in about 30 seconds [20].…”

Section: Search-based Software Modularizationmentioning

confidence: 99%

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Splitting a large software repository for easing future software evolution—an industrial experience report

Glorie

Zaidman

Deursen

et al. 2009

J. Softw. Maint. Evol.: Res. Pract.

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SUMMARYPhilips Medical Systems produces medical diagnostic imaging products, such as MR, X-ray and CT systems. The software of these devices is complex, has been evolving for several decades and is currently a multi-MLOC monolithic software repository. In this paper we report on splitting a single software repository into multiple smaller repositories so that these can be developed independently, easing the software's evolution. For splitting the single software repository, we set up two experiments that involve well-known analysis techniques, namely formal concept analysis and clustering. Because of the sheer size of the monolithic software repository, we also propose to use a 'leveled approach', which implies that the analysis technique is applied in several iterations, whereby in some iterations only part of the application is subjected to the analysis technique. Unfortunately, both analysis techniques failed to produce an acceptable partitioning of the monolithic software repository, even if they are combined with our newly proposed leveled approach. We provide a number of valuable lessons learned, which might prevent others from falling into the same pitfalls.

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Section: Related Workmentioning

confidence: 99%

Section: Search-based Software Modularizationmentioning

confidence: 99%

Splitting a large software repository for easing future software evolution—an industrial experience report

Glorie

Zaidman

Deursen

et al. 2009

J. Softw. Maint. Evol.: Res. Pract.

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“…Wierda et al [21] present a case study to recover subsystems of a legacy software by clustering static relations of classes using different versions of the system. They show that using different versions of the system as an input can improve the accuracy of the results.…”

Section: Recovering Software Componentsmentioning

confidence: 99%

Assessing software archives with evolutionary clusters

Vanya¹,

Holland

Klusener³

et al. 2008

2008 16th IEEE International Conference on Program Comprehension

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The way in which a system's software archive is partitioned influences the evolvability of that system. The partition of a software archive is mostly assessed by looking at the static (include, call) relations between the parts. In the literature history information is also taken into account to assess the partition but only pairs of software entities are related. In this paper we describe a novel history-based approach to assess the extent in which a certain partition allows its parts to evolve independently. We use the assumption that a set of software entities which co-evolved often in the past are likely to be modified together in the near future as well. Hence, the elements of such a set should in principle belong to the same part. Our approach, therefore, identifies sets of co-evolving software entities, where each set has elements from more than one part of the archive. We illustrate our approach with a case study of a large software system that evolved during more than a decade, and has over 7 million lines of code.

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“…Common clustering approaches retrieve this information directly from the static source code in form of structural dependencies based on, for example, method invocations and variable references among methods [1], [12], [14], or inheritance, aggregation, and method invocations among classes [17], [24]. Some approaches try to improve the clustering by taking dynamic code dependencies recorded during the program execution into consideration [6], [26].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of Software Evolution on Software Clustering

Beck

Diehl

2010

2010 17th Working Conference on Reverse Engineering

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The evolution of a software project is a rich data source for analyzing and improving the software development process. Recently, several research groups have tried to cluster source code artifacts based on information about how the code of a software system evolves. The results of these evolutionary approaches seem promising, but a direct comparison to traditional software clustering approaches based on structural code dependencies is still missing. To fill this gap, we conducted several clustering experiments with an approved software clustering tool comparing and combining the evolutionary and the structural approach. These experiments show that the evolutionary approach could produce meaningful clustering results. But still the traditional approach provides better results because of a more reliable data density of the structural data. Finally, the combination of both approaches is able to improve the overall clustering quality.

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Using version information in architectural clustering - a case study

Cited by 9 publications

References 34 publications

Splitting a large software repository for easing future software evolution—an industrial experience report

Splitting a large software repository for easing future software evolution—an industrial experience report

Assessing software archives with evolutionary clusters

Evaluating the Impact of Software Evolution on Software Clustering

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