On Proving the Correctness of Refactoring Class Diagrams of MDE Metamodels

Altoyan, Najd; Batory, Don

doi:10.1145/3549541

Cited by 2 publications

(2 citation statements)

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“…A metamodel refactoring is an invertible semantics-preserving co-transformation that alters a metamodel as well as its models without losing data. Alton et al [36] proposed a meta-modeling approach for pattern-based refactoring using design pattern and transformation rule specification to make the software more modular, modifiable, and reusable. This work [36] addresses a subproblem of metamodel refactoring: how to use the Coq Proof Assistant to show the validity of the refactoring of class diagrams without OCL constraints.…”

Section: Literature Surveymentioning

confidence: 99%

“…Alton et al [36] proposed a meta-modeling approach for pattern-based refactoring using design pattern and transformation rule specification to make the software more modular, modifiable, and reusable. This work [36] addresses a subproblem of metamodel refactoring: how to use the Coq Proof Assistant to show the validity of the refactoring of class diagrams without OCL constraints. Leandro et al [37] considered identifying bad smells as a feature to perform a refactoring assessment, where the distance between system entities (attributes/methods) and class extracts with certain pre-conditioning was used to perform behavior-preserving refactoring analysis.…”

Section: Literature Surveymentioning

confidence: 99%

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Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

et al. 2022

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Background: Refactoring is changing a software system without affecting the software functionality. The current researchers aim i to identify the appropriate method(s) or class(s) that needs to be refactored in object-oriented software. Ensemble learning helps to reduce prediction errors by amalgamating different classifiers and their respective performances over the original feature data. Other motives are added in this paper regarding several ensemble learners, errors, sampling techniques, and feature selection techniques for refactoring prediction at the class level. Objective: This work aims to develop an ensemble-based refactoring prediction model with structural identification of source code metrics using different feature selection techniques and data sampling techniques to distribute the data uniformly. Our model finds the best classifier after achieving fewer errors during refactoring prediction at the class level. Methodology: At first, our proposed model extracts a total of 125 software metrics computed from object-oriented software systems processed for a robust multi-phased feature selection method encompassing Wilcoxon significant text, Pearson correlation test, and principal component analysis (PCA). The proposed multi-phased feature selection method retains the optimal features characterizing inheritance, size, coupling, cohesion, and complexity. After obtaining the optimal set of software metrics, a novel heterogeneous ensemble classifier is developed using techniques such as ANN-Gradient Descent, ANN-Levenberg Marquardt, ANN-GDX, ANN-Radial Basis Function; support vector machine with different kernel functions such as LSSVM-Linear, LSSVM-Polynomial, LSSVM-RBF, Decision Tree algorithm, Logistic Regression algorithm and extreme learning machine (ELM) model are used as the base classifier. In our paper, we have calculated four different errors i.e., Mean Absolute Error (MAE), Mean magnitude of Relative Error (MORE), Root Mean Square Error (RMSE), and Standard Error of Mean (SEM). Result: In our proposed model, the maximum voting ensemble (MVE) achieves better accuracy, recall, precision, and F-measure values (99.76, 99.93, 98.96, 98.44) as compared to the base trained ensemble (BTE) and it experiences less errors (MAE = 0.0057, MORE = 0.0701, RMSE = 0.0068, and SEM = 0.0107) during its implementation to develop the refactoring model. Conclusions: Our experimental result recommends that MVE with upsampling can be implemented to improve the performance of the refactoring prediction model at the class level. Furthermore, the performance of our model with different data sampling techniques and feature selection techniques has been shown in the form boxplot diagram of accuracy, F-measure, precision, recall, and area under the curve (AUC) parameters.

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Section: Literature Surveymentioning

confidence: 99%