Efficient software clustering technique using an adaptive and preventive dendrogram cutting approach

Chong, Chun Yong; Lee, Sai Peck; Ling, Teck Chaw

doi:10.1016/j.infsof.2013.07.002

Cited by 30 publications

(21 citation statements)

References 25 publications

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“…There are a number of studies focusing on this area [10,[34][35][36][37][38]. Program clustering is one of the effective ways for program comprehension.…”

Section: Related Workmentioning

confidence: 99%

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Using Hierarchical Latent Dirichlet Allocation to Construct Feature Tree for Program Comprehension

Sun

Liu

Duan

et al. 2017

Scientific Programming

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Program comprehension is an important task faced by developers during software maintenance. With the increasing complexity of evolving systems, program comprehension becomes more and more difficult. In practice, programmers are accustomed to getting a general view of the features in a software system and then finding the interesting or necessary files to start the understanding process. Given a system, developers may need a general view of the system. The traditional view of a system is shown in a packageclass structure which is difficult to understand, especially for large systems. In this article, we focus on understanding the system in both feature view and file structure view. This article proposes an approach to generate a feature tree based on hierarchical Latent Dirichlet Allocation (hLDA), which includes two hierarchies, the feature hierarchy and file structure hierarchy. The feature hierarchy shows the features from abstract level to detailed level, while the file structure hierarchy shows the classes from whole to part. Empirical results show that the feature tree can produce a view for the features and files, and the clustering of classes in the package in our approach is better (in terms of recall) than the other clustering approach, that is, hierarchical clustering.

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“…There are a number of studies focusing on this area [10,[34][35][36][37][38]. Program clustering is one of the effective ways for program comprehension.…”

Section: Related Workmentioning

confidence: 99%

“…To reduce the difficulty of program comprehension, one of the effective approaches is to create a meaningful decomposition of large-scale system into smaller, more manageable subsystems, which is called software clustering [10][11][12]. A number of program comprehension techniques have been studied [13,14].…”

Section: Introductionmentioning

confidence: 99%

Using Hierarchical Latent Dirichlet Allocation to Construct Feature Tree for Program Comprehension

Sun

Liu

Duan

et al. 2017

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Depending on where the tree is cut the resulting clusters vary both in size and amount. Researchers use different approaches when it comes to finding where to cut, and research has been done to find the best cutting point [22]. Garcia et al chose to use the ground truth to find the optimal cut, which, while valid for their comparison, we find counterproductive, since usually the ground truth is unavailable when reverse-engineering a system [15].…”

Section: ) Construct Validitymentioning

confidence: 99%

On the significance of relationship directions in clustering algorithms for reverse engineering

Jensen

Lundkvist

Hammouda

2017

Proceedings of the Symposium on Applied Computing

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“…Finding a solution to minimize the objective function in (2) is an optimization problem with constraints in (3). Based on simple optimization theory, we can obtain the update formula of membership and cluster center for each iteration as shown in (4), and (5).…”

Section: Traditional Fcm Clusteringmentioning

confidence: 99%

“…In addition, diverse factors can contribute coupling among software components, including functional and non-functional requirements, user and system constraints, and legacy reasons. On the other hand, the works on software clustering mostly rely on a single method for membership value, e.g., distanceonly, calculation [3], which is difficult to identify the complex coupling relationships among software components. The research on software clustering lags behind other disciplines, e.g., pattern recognition, where multiple measurements have been used, in addition to distance calculation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Clustering Techniques for Software Components and Architecture

Liu

Lung

Ajila

2015

2015 IEEE 39th Annual Computer Software and Applications Conference

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Software components analysis is a critical technique for software development and maintenance. Clustering techniques have been widely used in grouping related software components. However, software is complex, but clustering techniques used in software engineering typically adopt only one metric to measure the similarity of components. This paper proposes an adaptive fuzzy clustering technique based on possibilistic clustering algorithms to address the issue of single metric. The proposed technique collaboratively considers distance, density, and the trend of density change of component instances in the membership degree calculation. The post clustering separation of clustered components based on the predefined thresholds and regrouping of the separated component points result in higher cohesive clustering. The proposed algorithm has been evaluated via experiments using a network protocol RSVP-TE system. The comparison of Hierarchical, Self-organizing map (SOM), and fuzzy c-means (FCM) against adaptive fuzzy clustering proposed in this paper indicates that the adaptive fuzzy clustering group software component instances into more cohesive clusters while it is also insensitive to parameter settings.

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Efficient software clustering technique using an adaptive and preventive dendrogram cutting approach

Cited by 30 publications

References 25 publications

Using Hierarchical Latent Dirichlet Allocation to Construct Feature Tree for Program Comprehension

Using Hierarchical Latent Dirichlet Allocation to Construct Feature Tree for Program Comprehension

On the significance of relationship directions in clustering algorithms for reverse engineering

Adaptive Clustering Techniques for Software Components and Architecture

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