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Elements (UMEs) in the model and Missing Model Elements (MMEs) in the text using the linkage information. ArDoCo’s approach shows promising results but has

Entity Recognition in Software Documentation Using Trace Links to Informal Diagrams Vortragende(r) Fabian Reinbold Vortragstyp

of its applicability in model-driven engineering (MDE). Especially when using textual domain-specific languages (DSL) for modeling, insights into the domain

meta-data set by characterizing base features using meta-features and quantifying their respective importance using model-agnostic feature importance measures

solution is to create a basic pretrained model using self-supervised objectives and then to fine-tune it using a smaller document-specific annotated dataset

instances of incompleteness was evaluated through a series of experiments using different datasets and various prompting techniques. The F1 score was employed

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detector JPlag. We show that this prevents automatic plagiarism generation using statement insertion and reordering. Additionally, we confirm that JPlag’s

approach to link elements from different artifacts is to identify similar words using word similarity measures. ArDoCo is a tool that uses word similarity measures

addition) using measures of displacement. We also investigate the stability of privacy measures toward load profile length and number of bins using specific

development. It enables the development of a wide range of applications using graphical tools, with little or no knowledge of text-based programming languages

applications. A prototypical implementation of the adapted approach was evaluated using real-world Lua-based sensor applications from the SICK AppSpace ecosystem

Kurzfassung Model-driven engineering (MDE) supports the development process by using domain-specific modeling languages (DSMLs). In industrial settings, DSMLs

Kurzfassung The goal of this bachelor thesis was to analyse classification results using a 2017 published method called shap. Explaining how an artificial neural

The main goal knowledge discovery focusses is, an increase of knowledge using some set of data. In many cases it is crucial that results are human-comprehensible

selection using meta-learning approaches. Meta-learning, also known as learning how to learn, serves as the primary tool for this study. First, by using the

Development of an Active Learning Approach for One Class Classification using Bayesian Uncertainty Vortragende(r) Tobias Haßberg Vortragstyp Proposal Betreuer(in)

models to primary development artifacts. In practice, MDSD is often realized using the Eclipse Modeling Framework (EMF). The Eclipse Modeling Framework depends

physically consistent results. Theory-Guided Data Science (TGDS) aims at using scientific knowledge to improve the effectiveness of Data Science models