Class Diagram Similarity Measurement: A Different Approach

Fauzan, Reza; Siahaan, Daniel; Rochimah, Siti; Triandini, Evi

doi:10.1109/icitisee.2018.8721021

Cited by 16 publications

(17 citation statements)

References 15 publications

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“…In this study, we developed an approach for automatically assessing the class diagram similarities as reliably as an expert can (i.e., a teacher who conducts assessments). This study is a continuation of previous studies [28] that only assessed semantic similarity. The proposed approach divides similarity into semantic and structural similarities.…”

Section: Introductionmentioning

confidence: 74%

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Automated Class Diagram Assessment using Semantic and Structural Similarities

Fauzan¹,

Siahaan²,

Rochimah³

et al. 2021

IJIES

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Class diagrams show classes in software and the relationships between those classes. A class diagram is a unified modeling language diagram commonly used in education. Thus, an assessment of class diagrams is essential for teachers who usually have students produce class diagrams based on predetermined projects. Teachers assess student-produced class diagrams based on an answer key. However, teachers have a problem with a lack of consistency in assessment as teachers can use different standards between answers. This research attempts to approach class diagram assessment automatically. The proposed approach consists of two assessments: semantic and structural similarities. Semantic similarity is calculated using lexical information in the class diagram, and structural similarity is calculated using the diagram's structure, ignoring its lexical information. Our results show that experts see semantic and structural similarities equally during assessment. The proposed approach shows substantial agreement with experts in class diagram similarity assessment. Therefore, the proposed approach can automatically assess class diagram similarity as reliably as experts can.

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Section: Introductionmentioning

confidence: 74%

“…Before measuring the similarity between two class diagrams, we first divided class diagram elements into property information and relationship information [28]. Fig.…”

Section: Class Diagram Semantic Similaritymentioning

confidence: 99%

Automated Class Diagram Assessment using Semantic and Structural Similarities

Fauzan¹,

Siahaan²,

Rochimah³

et al. 2021

IJIES

Self Cite

View full text Add to dashboard Cite

show abstract

“…Graph Representation of s2 [23] proposed an algorithm based on greedy the algorithm, which is superior in matching time compared with the simulated annealing based algorithm. This method then adapted by several researchers such as [25,21] for measuring structural and semantic similarity.…”

Section: Greedy Algorithmmentioning

confidence: 99%

“…The result shows that u 1 : g 1 is best matched with u 1 : g 2 , u 2 : g 1 is best matched with u 4 : g 2 , u 3 : g 1 is best matched with u 3 : g 2 , and u 5 : g 1 is best matched with u 2 : g 2 . Given the best pairs, we could calculate the structural similarity measurement of use cases in g 1 and g 1 as follow: Given the structural similarity score of actors and use cases, we could calculate the structural similarity between g 1 and g 2 as follow: [16] and [25], this paper does not use cosine similarity for semantic similarity calculation. We could calculate the semantic similarity between pairs of actors as follow:…”

Section: Structural Similarity Measurementmentioning

confidence: 99%

Structural and Semantic Similarity Measurement of UML Use Case Diagram

Arifin

Siahaan

2020

LKJITI

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Reusing software has several benefits ranging from reducing cost and risk, accelerating development, and its primary purposes are improving software quality. In the early stage of software development, reusing existing software artifacts may increase the benefit of reusing software because it uses mature artifacts from previous artifacts. One of software artifacts is diagram, and in order to assist the reusing diagram is to find the level of similarity of diagrams. This paper proposes a method for measuring the similarity of the use case diagram using structural and semantic aspects. For structural similarity measurement, Graph Edit Distance is used by transforming each factor and use case into a graph, while for semantic similarity measurement, WordNet, WuPalmer,and Levenshtein were used. The experimentation was conducted on ten datasets from variousprojects. The results of the method were compared with the results of assessments from experts.The measurement of agreement between experts and method was done by using Gwet’s AC1 andPearson correlation coefficient. Measurement results with Gwet’s AC1 diagram similarity are 0,60,which were categorized as “moderate" agreement and the result of measurement with Pearsonis 0.506 which means there is a significant correlation between experts and methods. The resultshowed that the proposed method can be used to find the similarity of the diagram, so finding andreuse of the diagram as a software component can be optimized.

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“…The way to assess use case diagrams is to measure the similarities between the student and answer key diagrams. Apart from use case diagrams, there are several other UML diagrams which can be measured in a similar way, such as class diagram [6,7], activity diagram [8], and sequence diagram [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Different Approach on Automated Use Case Diagram Semantic Assessment

Fauzan¹,

Siahaan²,

Rochimah³

et al. 2021

IJIES

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The use case diagram is one of the diagrams commonly taught in colleges of computer science. Assessment of use case diagrams is often an obstacle for a teacher in the learning process. It is due to the interpersonal and intrapersonal problems of the teacher in assessing. Interpersonal problems are caused by the absence of an assessment standard among teachers. Intrapersonal problems are caused by the inconsistency of a teacher in assessing many diagrams of student answers. This research aims to create a semantic use case diagram automatic assessment method. Semantic assessment is divided into two kinds, namely property and relationship. All information used is a label translated from the XMI document. Similarity assessment between labels used cosine similarity, employing WuPalmer to perform WordNet searches. The results showed that the proposed method had a substantial agreement with the teacher as an expert; however, a teacher tends to look at property information rather than relationship information to assess use case diagrams.

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Class Diagram Similarity Measurement: A Different Approach

Cited by 16 publications

References 15 publications

Automated Class Diagram Assessment using Semantic and Structural Similarities

Automated Class Diagram Assessment using Semantic and Structural Similarities

Structural and Semantic Similarity Measurement of UML Use Case Diagram

A Different Approach on Automated Use Case Diagram Semantic Assessment

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