Related papers: Solving the TTC 2011 Model Migration Case with Eda…

The challenge of the Compiler Optimization Case is to perform local optimizations and instruction selection on the graph-based intermediate representation of a compiler. The case is designed to compare participating tools regarding their…

This paper presents a solution to the Flowgraphs case study for the Transformation Tool Contest 2013 (TTC 2013). Starting from Java source code, we execute a chain of model transformations to derive a simplified model of the program, its…

This case comprises several primitive tasks that can be solved straight away with most transformation tools. The aim is to cover the most important kinds of primitive operations on models, i.e. create, read, update and delete (CRUD). To…

This paper discusses the GReTL solution of the TTC 2011 Hello World case. The submitted solution covers all tasks including the optional ones.

In automatic post-editing (APE) it makes sense to condition post-editing (pe) decisions on both the source (src) and the machine translated text (mt) as input. This has led to multi-source encoder based APE approaches. A research challenge…

Model repair is an essential topic in model-driven engineering. We present typed graph-repair programs for specific conditions; application to any typed graph yields a typed graph satisfying the condition. A model graph based on the Eclipse…

This paper describes the solution of Hello World transformations in MOLA transformation language. Transformations implementing the task are relatively straightforward and easily inferable from the task specification. The required additional…

Recent approaches to the Automatic Post-Editing (APE) research have shown that better results are obtained by multi-source models, which jointly encode both source (src) and machine translation output (mt) to produce post-edited sentence…

adaptNMT is an open-source application that offers a streamlined approach to the development and deployment of Recurrent Neural Networks and Transformer models. This application is built upon the widely-adopted OpenNMT ecosystem, and is…

This report presents a partial solution to the Compiler Optimization case study using GROOVE. We explain how the input graphs provided with the case study were adapted into a GROOVE representation and we describe an initial solution for…

In mutation testing the question whether a mutant is equivalent to its program is important in order to compute the correct mutation score. Unfortunately, answering this question is not always possible and can hardly be obtained just by…

The training of topic models for a multilingual environment is a challenging task, requiring the use of sophisticated algorithms, topic-aligned corpora, and manual evaluation. These difficulties are further exacerbated when the developer…

As is known, tetrahedron equations lead to the commuting family of transfer-matrices and provide the integrability of corresponding three-dimensional lattice models. We present the modified version of these equations which give the…

Epsilon is an extensible platform of integrated and task-specific languages for model management. With solutions to the 2011 TTC Hello World case, this paper demonstrates some of the key features of the Epsilon Object Language (an extension…

Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a…

Gradually typed languages allow programmers to mix statically and dynamically typed code, enabling them to incrementally reap the benefits of static typing as they add type annotations to their code. However, this type migration process is…

The Eclipse Graphical Modeling (GMF) Framework provides the major approach for implementing visual languages on top of the Eclipse platform. GMF relies on a family of modeling languages to describe different aspects of the visual language…

The inference of large language models imposes significant computational workloads, often requiring the processing of billions of parameters. Although early-exit strategies have proven effective in reducing computational demands by halting…

The embedded topic model (ETM) is a widely used approach that assumes the sampled document-topic distribution conforms to the logistic normal distribution for easier optimization. However, this assumption oversimplifies the real…

Text image machine translation (TIMT) aims to translate texts embedded in images from one source language to another target language. Existing methods, both two-stage cascade and one-stage end-to-end architectures, suffer from different…