Discrete-Event Simulation Model Generation based on Activity Metrics

Capocchi, Laurent; Santucci, Jean-François; Pawletta, Thorsten; Folkerts, Hendrik; Zeigler, Bernard P.

doi:10.1016/j.simpat.2020.102122

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…The McCabe CC metric [14] typically corresponds to the size of a method since every method contains several branches distributed throughout its code [15]. Kalagara [15] provides an overview of CC and its computation using flow graphs in software development.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring Cyclomatic Complexity of Source Code Using Machine Learning

Odeh,

Odeh

et al. 2024

RIA

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High-performance, high-quality software is a fundamental goal for all developers. To achieve this, software needs to be built with exceptional quality, featuring a simplified structure, strong coherence, and minimized complexity. Cyclomatic Complexity (CC), critical software metric, quantifies the intricacy of a program's structure and control flow. Traditionally, CC measurement has relied on laborious manual techniques or conventional programming methods, both prone to errors and inefficiency. To overcome these limitations, we present a revolutionary approach that leverages a Multinomial Naive Bayes (MNB) machine learning (ML) algorithm for automated CC measurement. This innovative method offers a more accurate, efficient, and reliable means of software complexity evaluation. Our study utilizes a vast dataset of 3,598 carefully curated programming samples from diverse programming projects across three popular languages: Java, Python, and C++. Training our MNB model on this comprehensive and diverse dataset yielded an outstanding overall accuracy of 97.3%, demonstrating the efficacy and reliability of our approach for CC measurement across different programming languages (PLs). This success can be attributed to the utilization of the NBM learning algorithm, known for its proficiency in classification tasks. Additionally, our study benefits from a larger and more diverse dataset compared to prior research, potentially contributing to the superior results. Our novel approach to CC measurement using machine learning holds significant promise for the development of more accurate and reliable code complexity assessment tools. These encouraging findings suggest that this approach has the potential to shape more effective software development practices in the future.

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Section: Literature Reviewmentioning

confidence: 99%

Measuring Cyclomatic Complexity of Source Code Using Machine Learning

Odeh,

Odeh

et al. 2024

RIA

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“…This approach enables activity scanning, counting the number of times an activity is activated [26]. Muzy, et al, tweaked the definition of activity to be, more generically, the number of events occurring during a simulation [27], and Capocchi, et al, employed this definition to develop a methodology for selecting the level of detail in a hierarchical simulation model [28].…”

Section: Complexity In Computer Sciencementioning

confidence: 99%

“…The invariance theorem of Kolmogorov complexity (see Chapter 2 of Li & Vitányi [53]) implies that these differences can be accounted for with an additive constant, but how often does the constant dominate, washing out any observability of complexity differences due to changes in model detail? How does using KC compare to the approach defined by Cappochi, et al [28], in using activity as the base metric for simulation complexity? Can KC of a DEVS model's event transition functions be substituted for activity in Cappochi's methodology?…”

mentioning

confidence: 99%

Towards a Simulation Model Complexity Measure

Thompson¹,

Hodson²,

Grimaila³

et al. 2023

Preprint

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Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. Key terms of model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes in model detail. Additional research in this area can advance the modeling and simulation body of knowledge toward the practical application of measuring simulation model complexity. Examples show that KC and NCD measurements of simulation models can detect changes in scope and detail.

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“…Model-driven approaches for modeling and simulation so far have mostly focused on the generation of (executable) simulation models [20][21][22][23], simulation on distributed architectures [24], or multi-formalism modeling [25].…”

Section: Related Workmentioning

confidence: 99%

A Model-Driven Approach for Conducting Simulation Experiments

et al. 2022

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With the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for them to be conducted. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility of simulation experiments. However, a more general approach that is suited for a variety of modeling and simulation areas, experiment types, and tools, which also allows for further automation, is still missing. Therefore, we present a novel model-driven engineering (MDE) framework for simulation studies that extends the state-of-the-art of conducting simulation experiments in the following ways: (a) Providing a structured representation of the various ingredients of simulation experiments in the form of meta models and collecting them in a repository improves knowledge sharing across application domains and simulation approaches. (b) Specifying simulation experiments in the quasi-standardized form of the meta models (e.g., via a GUI) and, subsequently, performing the automatic generation of experiment specifications in a language of choice increases both the productivity and quality of complex simulation experiments. (c) Automatic code transformation between specification languages via the meta models enables the reusability of simulation experiments. (d) Integrating the framework using a command-line interface allows for further automation of subprocesses within a simulation study. We demonstrate the advantages and practicality of our approach using real simulation studies from three different fields of simulation (stochastic discrete-event simulation of a cell signaling pathway, virtual prototyping of a neurostimulator, and finite element analysis of electric fields) and various experiment types (global sensitivity analysis, time course analysis, and convergence testing). The proposed framework can be the starting point for further automation of simulation experiments and, therefore, can assist in conducting simulation studies in a more systematic and effective manner. For example, based on this MDE framework, approaches for automatically selecting and parametrizing experimentation methods, or for planning follow-up activities depending on the context of the simulation study, could be developed.

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Discrete-Event Simulation Model Generation based on Activity Metrics

Cited by 9 publications

References 31 publications

Measuring Cyclomatic Complexity of Source Code Using Machine Learning

Measuring Cyclomatic Complexity of Source Code Using Machine Learning

Towards a Simulation Model Complexity Measure

A Model-Driven Approach for Conducting Simulation Experiments

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