Similarity assessment of UML class diagrams using simulated annealing

Al-Khiaty, Mojeeb Al-Rhman; Ahmed, Moataz

doi:10.1109/icsess.2014.6933505

Cited by 19 publications

(9 citation statements)

References 20 publications

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“…Eq. (17) shows how to calculate intraSim. The information in each class contains numbers of attributes, operations, and parameters.…”

Section: Ucg Similarity Assessmentmentioning

confidence: 99%

“…This study showed that combining class structures and class properties is a good approach for calculating similarity. Al-Khiaty and Ahmed [14][15][16][17] measured similarities of class properties and class diagram structures. He combined class names and information within the class (e.g., attribute and operation names) as class properties.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Eq. (17) shows how to calculate intraSim. The information in each class contains numbers of attributes, operations, and parameters.…”

Section: Ucg Similarity Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Class Diagram Assessment using Semantic and Structural Similarities

Fauzan¹,

Siahaan²,

Rochimah³

et al. 2021

IJIES

View full text Add to dashboard Cite

show abstract

“…Kolovos et al [86] analyzed existing model matching approaches, and split them into four categories, based on how the matching is conducted. Their work is one of the earliest takes on categorizing model matching algorithms, and many later studies were influenced by it [6,30,158,159]. This categorization also serves as the basis of our survey.…”

Section: Background On Model Matchingmentioning

confidence: 99%

“…The evaluation and benchmarking of model matching, differencing, and merging algorithms is considered to be a difficult task in the literature [8,152]. Most approaches that we found focus on the accuracy of the matching process in some way [1,[4][5][6][7]27,44,47,48,108,109,152,158,161,169]. Metrics like precision, recall, or F-measure that are known from pattern recognition and machine learning [26] are often used to measure accuracy.…”

Section: Evaluation Techniques (Rq2)mentioning

confidence: 99%

Systematic review of matching techniques used in model-driven methodologies

Somogyi

Asztalos

2019

Softw Syst Model

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In model-driven methodologies, model matching is the process of finding a matching pair for every model element between two or more software models. Model matching is an important task as it is often used while differencing and merging models, which are key processes in version control systems. There are a number of different approaches to model matching, with most of them focusing on different goals, i.e., the accuracy of the matching process, or the generality of the algorithm. Moreover, there exist algorithms that use the textual representations of the models during the matching process. We present a systematic literature review that was carried out to obtain the state-of-the-art of model matching techniques. The search process was conducted based on a well-defined methodology. We have identified a total of 3274 non-duplicate studies, out of which 119 have been included as primary studies for this survey. We present the state-of-the-art of model matching, highlighting the differences between different matching techniques, mainly focusing on text-based and graph-based algorithms. Finally, the main open questions, challenges, and possible future directions in the field of model matching are discussed, also including topics like benchmarking, performance and scalability, and conflict handling. Keywords Model matching • Model comparison • Model differencing • Version control • Text-based modeling • Systematic literature review Communicated by Professor Jon Whittle.

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“…Different metrics as well as different matching algorithms have been proposed in the literature to identify the similarity and the differences of the models to be matched, especially for UML diagrams [6]- [11]. In prior works, we presented the use of Simulated Annealing algorithm (SA) [12] and the greedy algorithm [13] for model matching using different similarity metrics. Two types of metrics were used, individual metrics (e.g., a metric which measures the similarity between two classes based on the lexical names of the two classes) and compound metrics (e.g.…”

mentioning

confidence: 99%

UML Class Diagrams: Similarity Aspects and Matching

Al-Khiaty¹,

Ahmed²

2016

LNSE

Self Cite

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Abstract-Model matching is at the core of different model management operations such as model evolution, consolidation, and retrieval. An accurate identification of the similarity and differences between the elements of the matched models leads to an accurate model matching, which, in turn, leads to better model management. Software metrics are the software engineer means to quantify the similarity between the elements of the matched models. In this paper, we empirically validate the use of different metrics for capturing the similarity and the differences between the elements of two matched UML class diagrams. The paper empirically investigates the improvement of the similarity assessment of the class diagrams through the weight calibration of compound metrics. The results, reported based on two case studies, show the superiority of the compound metrics over the individual metrics.Index Terms-Model matching, similarity metrics, reuse, weight calibration.

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Similarity assessment of UML class diagrams using simulated annealing

Cited by 19 publications

References 20 publications

Automated Class Diagram Assessment using Semantic and Structural Similarities

Automated Class Diagram Assessment using Semantic and Structural Similarities

Systematic review of matching techniques used in model-driven methodologies

UML Class Diagrams: Similarity Aspects and Matching

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