In this paper we show how our approach of extending Language Driven Engineering (LDE) with natural language-based code generation supports system migration: The characteristic decomposition of LDE into tasks that are ...
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This paper presents an approach to no-code development based on the interplay of formally defined (graphical) Domain-Specific Languages and informal, intuitive Natural Language which is enriched with contextual inform...
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We present an introduction to the usage of Rig, our Cinco product for the graphical modeling of CI/CD workflows. While CI/CD has become a de facto standard in modern software engineering (e.g. DevOps) and the benefits...
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We present Pyrus, a domain-specific online modeling environment for building graphical processes for data analysis, machine learning and artificial intelligence. Pyrus aims at bridging the gap between de facto (often ...
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Collaborative system development requires a three-dimensional alignment: in space, in time, and in mindset: Traditionally, different developers typically have their own, local development environments, each of which m...
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Within the field of Requirements Engineering (RE), the increasing significance of Explainable Artificial Intelligence (XAI) in aligning AI-supported systems with user needs, societal expectations, and regulatory stand...
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Within the field of Requirements Engineering (RE), the increasing significance of Explainable Artificial Intelligence (XAI) in aligning AI-supported systems with user needs, societal expectations, and regulatory stand...
Within the field of Requirements Engineering (RE), the increasing significance of Explainable Artificial Intelligence (XAI) in aligning AI-supported systems with user needs, societal expectations, and regulatory standards has garnered recognition. In general, explainability has emerged as an important non-functional requirement that impacts system quality. However, the supposed trade-off between explainability and performance challenges the presumed positive influence of explainability. If meeting the requirement of explainability entails a reduction in system performance, then careful consideration must be given to which of these quality aspects takes precedence and how to compromise between them. In this paper, we critically examine the alleged trade-off. We argue that it is best approached in a nuanced way that incorporates resource availability, domain characteristics, and considerations of risk. By providing a foundation for future research and best practices, this work aims to advance the field of RE for AI.
A difficulty in processing of the natural language is recognizing the context of a statement. However, since this contains implicit knowledge which we unconsciously use in our formulations, it is necessary to assign t...
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ISBN:
(数字)9781728163321
ISBN:
(纸本)9781728163338
A difficulty in processing of the natural language is recognizing the context of a statement. However, since this contains implicit knowledge which we unconsciously use in our formulations, it is necessary to assign the context for correct interpretation. A context in our system is managed by a service that interprets and processes input and provides feedback to the end user. In this paper, we present a solution how an end user input can be assigned to such a service. For this purpose, we score the end user inputs by the system with an ensemble of 6 different classifiers that consider semantics as well as syntax. The system learns the user's input at run time and adapts his enunciation step by step. During the evaluation, the system was able to classified 87% of the user statements to the correct service. Far from perfect, this research might lead to fundamental changes in computer use.
Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise is the outcome of their predictions. However, this comes at a cost: their running time for classificati...
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The paper considers domain-specific tool support as a means to turn descriptive into prescriptive models, and to blur the difference between models and programs, and even between developers and users. Conceptual under...
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