Condensing class diagrams by analyzing design and network metrics using optimistic classification

Thung, Ferdian; Lo, David; Osman, Mohd Hafeez; Chaudron, Michel R. V.

doi:10.1145/2597008.2597157

Cited by 36 publications

(25 citation statements)

References 31 publications

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“…Closeness centrality [37][38][39][40][41][42][43][44] is defined as the average shortest path length between a particular vertex v and other nodes in the graph G. High values of closeness centrality in the case described in this text mean that the concrete semantic resource is close to other resources. It causes simple movement through the network of resources and acquiring of new information.…”

Section: Resultsmentioning

confidence: 99%

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Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Čerba

Jedlička

Čada

et al. 2017

IJGI

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Clear and straightforward communication is a key aspect of all human activities related to crisis management. Since crisis management activities involve professionals from various disciplines using different terminology, clear and straightforward communication is difficult to achieve. Semantics as a broad science can help to overcome communication difficulties. This research focuses on the evaluation of available semantic resources including ontologies, thesauri, and controlled vocabularies for disaster risk reduction as part of crisis management. The main idea of the study is that the most appropriate source of broadly understandable terminology is such a semantic resource, which is accepted by-or at least connected to the majority of other resources. Important is not only the number of interconnected resources, but also the concrete position of the resource in the complex network of Linked Data resources. Although this is usually done by user experience, objective methods of resource semantic centrality can be applied. This can be described by centrality methods used mainly in graph theory. This article describes the calculation of four types of centrality methods (Outdegree, Indegree, Closeness, and Betweenness) applied to 160 geographic concepts published as Linked Data and related to disaster risk reduction. Centralities were calculated for graph structures containing particular semantic resources as nodes and identity links as edges. The results show that (with some discussed exceptions) the datasets with high values of centrality serve as important information resources, but they also include more concepts from preselected 160 geographic concepts. Therefore, they could be considered as the most suitable resources of terminology to make communication in the domain easier. The main research goal is to automate the semantic resources evaluation and to apply a well-known theoretical method (centrality) to the semantic issues of Linked Data. It is necessary to mention the limits of this study: the number of tested concepts and the fact that centralities represents just one view on evaluation of semantic resources.

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Section: Resultsmentioning

confidence: 99%

“…Betweenness centrality [13,31,37,[39][40][41]43,44] is defined in terms of how "inbetween" a vertex is among the other vertices in the graph [14]. High values of the betweenness centrality in the network of semantic resources mean that the node could be a "bridge" among several independent (not directly interconnected) parts of the network.…”

Section: Resultsmentioning

confidence: 99%

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Čerba

Jedlička

Čada

et al. 2017

IJGI

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“…Thung et al and Yang et al classified whether each class in a reverse-engineered class diagram was important with their original classifier using various metrics (e.g., design and network metrics) [28], [29]. They used the classifier and thereby obtained a condensed version of a reverseengineered class diagram that was close to a forward designed one.…”

Section: Identifying Important Classes Of a Software Systemmentioning

confidence: 99%

Identifying Core Objects for Trace Summarization by Analyzing Reference Relations and Dynamic Properties

Noda

Kobayashi

Atsumi

2018

IEICE Trans. Inf. & Syst.

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Kunihiro NODA†a) , Nonmember, Takashi KOBAYASHI †b) , and Noritoshi ATSUMI † †c) , Members SUMMARY Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often afflicted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactified reverseengineered sequence diagram that contains system's key behaviors. To identify core objects, first, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more effective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-theart trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a significant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.

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“…This approach has been very effective in identifying important entities in many domains including bioinformatics [18] and epidemiology [37] as well as in other research papers on modeling software as networks [46,29,20,38] as discussed in Sec. 2.…”

Section: Analysis Strategy Based On Network Metricsmentioning

confidence: 99%

“…Perin et al [29] use a PageRank-based algorithm, computing importance of classes or methods based on the number of incoming references. Thung et al [38] propose a machine learning approach utilizing a combination of coupling and network metrics to identify important classes to include in a reverse engineered class diagram. We compare and contrast many of the same network metrics against the K-core values in Sec.…”

Section: Identifying Core/periphery Structurementioning

confidence: 99%

Identifying Important Classes of Large Software Systems Through K-Core Decomposition

Meyer

Siy

Bhowmick

2014

Advs. Complex Syst.

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In a large software project, the number of classes, and the dependencies between them, generally increase as software evolves. The size and scale of the system often makes it difficult to easily identify the important components in a particular software product. To address this problem, we model software as a network, where the classes are the vertices in the network and the dependencies are the edges, and apply K-core decomposition to identify a core subset of vertices as potentially important classes. We study three open source Java projects over a 10-year period and demonstrate, using different metrics, that the K-core decomposition of the network can help us identify the key classes of the corresponding software. Specifically, we show that the vertices with the highest core number represent the important classes and demonstrate that the core-numbers of classes with similar functionalities evolve at similar trends.

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Condensing class diagrams by analyzing design and network metrics using optimistic classification

Cited by 36 publications

References 31 publications

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Identifying Core Objects for Trace Summarization by Analyzing Reference Relations and Dynamic Properties

Identifying Important Classes of Large Software Systems Through K-Core Decomposition

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