Software Clustering Using Dynamic Analysis and Static Dependencies

Patel, C. R.; Hamou-Lhadj, Abdelwahab; Rilling, Juergen

doi:10.1109/csmr.2009.62

Cited by 47 publications

(45 citation statements)

References 21 publications

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“…The literature includes a number of approaches and techniques that are supported by software clustering and that can facilitate the recovery of information from a software system [10,12,39,56]. Two examples of typical reverse engineering are module recovery [12,18,23,29] and architecture recovery [10,39,48,58,70]. Module recovery software clustering methods focus on recovering modules based on an analysis of dependencies extracted from the software system, such as function calls.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…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%

“…Clustering, an unsupervised learning technique has been widely adopted for software architecture recovery [6,8,18,19,20]. Clustering recovers the architecture for a certain version by rebuilding the architecture (grouping together similar entities), and does not rely on the existing architectural information.…”

Section: Introductionmentioning

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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“…Patel's study [6] uses static analysis first to extract structural dependency and refines the dependency using the dynamic analysis. Garcia et al [10] combine structural and textual properties to construct feature vectors of software entities.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Weighting of Structural Dependency and Textual Similarity in Software Architecture Recovery

Lee

2016

IEICE Trans. Inf. & Syst.

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SUMMARYSoftware architecture recovery techniques are often adopted to derive a module view of software from its source code in case software architecture documents are unavailable or outdated. The module view is one of the most important perspectives of software architecture. In this paper, we propose a novel approach to derive a module view by adaptively integrating structural dependency and textual similarity. Our approach utilizes Newman modularity and Shannon information entropy to determine the appropriate weights of the dependencies during the integration. We apply our approach to various open-source projects and show the experimental results validating the effectiveness of the approach. key words: software architecture recovery, module view, dependency, Newman modularity, information entropy

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Software Clustering Using Dynamic Analysis and Static Dependencies

Cited by 47 publications

References 21 publications

Cooperative based software clustering on dependency graphs

Cooperative based software clustering on dependency graphs

Supervised Learning For Orphan Adoption Problem In Software Architecture Recovery

Adaptive Weighting of Structural Dependency and Textual Similarity in Software Architecture Recovery

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