Formal Ontology Driven Model Refactoring

Singh, Neeraj Kumar; Aït‐Ameur, Yamine; Méry, Dominique

doi:10.1109/iceccs2018.2018.00022

Cited by 5 publications

(5 citation statements)

References 23 publications

(45 reference statements)

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“…First, Event‐B and ProB have been successfully used to model interactive systems and validation of user tasks 34‐36 . Domain knowledge properties and model annotations are also supported by Event‐B 37‐40 . The EB2ALL 41‐43 tool is used for code generation when the suited refinement level is reached.…”

Section: F3fluid (Formal Framework For Fluid)mentioning

confidence: 99%

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F3FLUID: A formal framework for developing safety‐critical interactive systems in FLUID

Singh

Aït‐Ameur

Mendil

et al. 2022

J Software Evolu Process

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This paper proposes a unified formal framework, Formal Framework For FLUID (F3FLUID), for the development of safety-critical interactive systems. This framework is based on the Formal Language of User Interface Design (FLUID) pivot modeling language defined in the FORMEDICIS project, which enables high-level system requirements for interactive systems to be specified in the FLUID language. This modeling language is specifically designed for handling concepts of safety-critical interactive systems, including domain knowledge. A FLUID model is used as a source model for the generation of several target models in different modeling languages to support the formal verification methods, such as theorem proving and model checking. In this paper, we use the Event-B modeling language for checking functional behaviors, user interactions, safety properties, and domain properties. A FLUID model is transformed into an Event-B model, and then, the Rodin tool is used to check the internal consistency with respect to the given safety properties. We illustrate the operational semantics of the FLUID language, and the transformation strategy of FLUID models into Event-B models, including the tool development. We use the ProB model checker to analyze the temporal properties and to animate the formalized specification. In addition, an interactive cooperative objects (ICOs) model is derived from the Event-B model for animation, visualization and validation of dynamic behaviors, visual properties, and task analysis. Finally, an industrial case study, complying with the ARINC 661 standard, Multi-Purpose Interactive Applications (MPIA), is used to illustrate the effectiveness of our F3FLUID framework for the development of safety-critical interactive systems.

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Section: F3fluid (Formal Framework For Fluid)mentioning

confidence: 99%

“…[34][35][36] Domain knowledge properties and model annotations are also supported by Event-B. [37][38][39][40] The EB2ALL [41][42][43] tool is used for code generation when the suited refinement level is reached. Lastly, scalability issues are addressed by proof-based techniques.…”

Section: Three Complementary Modeling Techniques: Event-b Prob and Icomentioning

confidence: 99%

F3FLUID: A formal framework for developing safety‐critical interactive systems in FLUID

Singh

Aït‐Ameur

Mendil

et al. 2022

J Software Evolu Process

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“…The authors used split lock, split critical section, and convert features to perform the refactoring. Singh et al [30] developed a refactoring-based pattern modeling concept to refine software design. The Rodin tool was applied to check the internal consistency concerning the desired functional behavior.…”

Section: Literature Surveymentioning

confidence: 99%

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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“…Design models annotation by domain-specific knowledge has been studied for state-based methods [5] as well. More recently, the textbook [6] reviewed many cases of exploiting explicit models of domain knowledge by system models spanning medical [31,40], e-voting [18], distributed systems etc.…”

Section: Standards Formalised As Ontologies ((1) On Fig 2)mentioning

confidence: 99%

Standard Conformance-by-Construction with Event-B

Mendil

Aït‐Ameur

Singh

et al. 2021

Formal Methods for Industrial Critical Systems

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Checking the conformance of a system design to a standard is a central activity in the system engineering life cycle, a fortiori when the concerned system is deemed critical. Standard conformance checking entails ensuring that a system or a model of a system faithfully meets the requirements of a specification of a standard improving the robustness and trustworthiness of the system model. In this paper, we present a formal framework based on the correct-by-construction Event-B method and related theories for formally checking the conformance of a formal system model to a formalised standard specification by construction. This framework facilitates the formalization of standard concepts and rules as an ontology, as well as the formalization of an engineering domain, using an Event-B theory consisting of data types and a collection of operators and properties. Conformance checking is accomplished by annotating the system model with typing conditions. We address an industrial case study borrowed from the aircraft cockpit engineering domain to demonstrate the feasibility and strengths of our approach. The ARINC 661 standard is formalised as an Event-B theory. This theory formally models and annotates the safety-critical real-world application of a weather radar system for certification purposes.

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Formal Ontology Driven Model Refactoring

Cited by 5 publications

References 23 publications

F3FLUID: A formal framework for developing safety‐critical interactive systems in FLUID

F3FLUID: A formal framework for developing safety‐critical interactive systems in FLUID

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

Standard Conformance-by-Construction with Event-B

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