Testing is the key activity in ensuring the quality of automotive systems. The corresponding test case specifications often contain test cases expressed in natural language. However, there is a lack of review approach...
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ISBN:
(纸本)9783031041150;9783031041143
Testing is the key activity in ensuring the quality of automotive systems. The corresponding test case specifications often contain test cases expressed in natural language. However, there is a lack of review approaches that are easy to apply for practitioners to ensure appropriate quality of those test case specifications. We therefore present an analytical quality assurance method based on a quality model and review checklists derived from it. Especially, we focus on quality criteria that are relevant for natural language test cases and in the context of the automotive domain. To ensure applicability in industrial practice, we stringently involve practitioners in the development of the quality model via expert workshops. The systematic derivation of quality characteristics results in a quality model for automotive test case specifications. Furthermore, we show how review checklists for a multidimensional review were derived from it. A first evaluation indicates that these review checklists support practitioners in conducting reviews and also foster the understanding of qualitative test case specifications.
CONTEXT: Modeling industrial systems is mostly done collaboratively. In such a scenario, a model is modified by multiple people over a possibly long period of time. In consequence, modelers have to be able to understa...
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ISBN:
(数字)9781665480925
ISBN:
(纸本)9781665480925
CONTEXT: Modeling industrial systems is mostly done collaboratively. In such a scenario, a model is modified by multiple people over a possibly long period of time. In consequence, modelers have to be able to understand a models evolution and, in particular, what elements of a model have changed, how, why, when, and by whom. Objective: We derive six distinct user goals and systematically design a graphical modeling languageagnostic set of tools that support users in achieving these goals. METHOD: We implement those tools and integrate them into an existing graphical modeling tool for technology roadmaps. To measure the tools usability, we conducted a mixed-methods study with participants of different levels of experience. With the set of developed tools at hand, in different scenarios, participants had to answer several questions regarding the evolution of a model. RESULTS: The SUS score for assessing usability ranged from 80 to 95, indicating good to excellent usability. Task completeness (measured per scenario) ranged from 92% to 100%. Further, participants emphasized the added value of the tools while completing the scenarios. CONCLUSION: Although participants stated that the set of tools can be classified as expert tools, the developed tools achieve the goal of enabling users to comprehend changes made by others and trace the impacts of operations.
We introduce the framework FreeCHR which formalizes the embedding of Constraint Handling Rules (CHR) into a host language, using the concept of initial algebra semantics from category theory, to establish a high-level...
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ISBN:
(纸本)9783031450716;9783031450723
We introduce the framework FreeCHR which formalizes the embedding of Constraint Handling Rules (CHR) into a host language, using the concept of initial algebra semantics from category theory, to establish a high-level implementation scheme for CHR as well as a common formalization for both theory and practice. We propose a lifting of the syntax of CHR via an endofunctor in the category Set and a lifting of the very abstract operational semantics of CHR into FreeCHR, using the free algebra, generated by the endofunctor and give proofs for soundness and completeness w.r.t. their original definition.
This paper presents a calculation of the aerodynamic characteristics of the ZOHD Alpha Strike UAV model, designed with a delta wing. The focus of the study is the overall layout of the aircraft and the influence of th...
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In this paper, we explain through a case study how to develop a digital twin that can be used for safety analysis of missions in physical contexts. More specifically, we consider a scenario where firefighters are oper...
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We present the multi-language software platform eknows for building reverse engineering tools and documentation generators as a concrete example of how to successfully translate research on software analysis into inno...
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We present the multi-language software platform eknows for building reverse engineering tools and documentation generators as a concrete example of how to successfully translate research on software analysis into innovative products and services. Platform development includes domain-specific requirements and an architecture supporting reuse of components.
Text classification tasks, particularly those involving a large number of features, pose significant challenges in effective feature selection. This research introduces a novel methodology, MBO-NB, which integrates Mi...
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Text classification tasks, particularly those involving a large number of features, pose significant challenges in effective feature selection. This research introduces a novel methodology, MBO-NB, which integrates Migrating Birds Optimization (MBO) approach with na & iuml;ve Bayes as an internal classifier to address these challenges. The motivation behind this study stems from the recognized limitations of existing techniques in efficiently handling extensive feature sets. Traditional approaches often fail to adequately streamline the feature selection process, resulting in suboptimal classification accuracy and increased computational overhead. In response to this need, our primary objective is to propose a scalable and effective solution that enhances both computational efficiency and classification accuracy in text classification systems. To achieve this objective, we preprocess raw data using the Information Gain algorithm, strategically reducing the feature count from an average of 62,221 to 2,089. Through extensive experiments, we demonstrate the superior effectiveness of MBO-NB in feature reduction compared to other existing techniques, resulting in significantly improved classification accuracy. Furthermore, the successful integration of na & iuml;ve Bayes within MBO offers a comprehensive and wellrounded solution to the feature selection problem. In individual comparisons with Particle Swarm Optimization (PSO), MBO-NB consistently outperforms by an average of 6.9% across four setups. This research provides valuable insights into enhancing feature selection methods, thereby contributing to the advancement of text classification techniques. By offering a scalable and effective solution, MBO-NB addresses the pressing need for improved feature selection methods in text classification, thereby facilitating the development of more robust and efficient classification systems.
Predictive maintenance relies on machine learning techniques to learn from historical data and also uses live data to analyse failure patterns. Different from conservative maintenance procedures that generally lead to...
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Predictive maintenance relies on machine learning techniques to learn from historical data and also uses live data to analyse failure patterns. Different from conservative maintenance procedures that generally lead to resource wastage, predictive maintenance can offer optimum resource utilisation and allow predict failures before they occur. Machine learning techniques are essential for automated predictive maintenance;therefore, in this paper the use and effectiveness of support vector machines for predictive maintenance is analysed. As the results show, support vector machines achieve the best performance when linear kernel function is used.
The vehicle-to-grid feature of today's electric vehicles suggests using them as batteries for stabilizing the power grid besides using them to fulfill mobility needs. In the context of car-sharing, the car-sharing...
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ISBN:
(纸本)9783031610332;9783031610349
The vehicle-to-grid feature of today's electric vehicles suggests using them as batteries for stabilizing the power grid besides using them to fulfill mobility needs. In the context of car-sharing, the car-sharing provider may thus try to foster two goals: they may be interested in stabilizing the grid and ensuring the usage of as much green energy as possible. At the same time, they try to maximize satisfaction of the customer's requests. As such, each car-sharing provider has to implement a policy on how to react to booking requests. On the other hand, customers may react to how mobility needs are fulfilled and adapt their booking strategy. In this paper, we study the problem of how to model elements of car-sharing providers as well as those of customers in a multi-agent simulation. We identify the principal elements and targets while leaving concrete simulations as future work.
Establishing digital twins is a non-trivial endeavor especially when users face significant challenges in creating them from scratch. Ready availability of re-usable models, data, functions, and tool assets, can help ...
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Establishing digital twins is a non-trivial endeavor especially when users face significant challenges in creating them from scratch. Ready availability of re-usable models, data, functions, and tool assets, can help with creation and use of digital twins. A number of frameworks/platforms exist to facilitate creation and use of digital twins. In this paper, we propose such a platform to manage digital twin assets, create composable digital twins from re-usable assets and make the digital twins available as a service to other users. The proposed platform supports the management of re-usable assets, storage, provision of compute infrastructure, communication, monitoring, and execution tasks. Two case studies are used to demonstrate the capabilities of this platform.
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