相关论文: Solving the TTC 2011 Reengineering Case with MOLA …

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…

The authors' "metatools" are a collection of tools for generic programming. This includes generating Java sources from mathematically well-founded specifications, as well as the creation of strictly typed document object models for XML…

This paper discusses the GReTL reference solution of the TTC 2011 Reengineering case. Given a Java syntax graph, a simple state machine model has to be extracted. The submitted solution covers both the core task and the two extension tasks.

The current paper presents a solution of the Program Understanding: A Reengineering Case for the Transformation Tool Contest using the VIATRA2 model transformation tool.

This paper presents LOLA, a massively multilingual large language model trained on more than 160 languages using a sparse Mixture-of-Experts Transformer architecture. Our architectural and implementation choices address the challenge of…

This paper presents the Henshin solution to the Model Transformations for Program Understanding case study as part of the Transformation Tool Contest 2011.

In Software Reengineering, one of the central artifacts is the source code of the legacy system in question. In fact, in most cases it is the only definitive artifact, because over the time the code has diverged from the original…

The aim of the Transformation Tool Contest (TTC) series is to compare the expressiveness, the usability and the performance of graph and model transformation tools along a number of selected case studies. Participants want to learn about…

Model-driven software engineering (MDE) techniques are not only useful in forward engineering scenarios, but can also be successfully applied to evolve existing systems. RAD (Rapid Application Development) platforms emerged in the nineties,…

Automatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance bottleneck and require more high-quality data for training. We propose a novel data…

Software systems are getting more and more complex. Model-driven engineering (MDE) offers ways to handle such increased complexity by lifting development to a higher level of abstraction. A key part in MDE are transformations that transform…

This case for the Transformation Tool Contest 2013 is about evaluating the scope and usability of transformation languages and tools for a set of four tasks requiring very different capabilities. One task deals with typical model-to-model…

In modern machine (ML) learning systems, Transformer-based architectures have achieved milestone success across a broad spectrum of tasks, yet understanding their operational mechanisms remains an open problem. To improve the transparency…

This paper gives an overview of the Edapt solution to the reengineering case of the Transformation Tool Contest 2011.

This paper addresses the issue of correcting type errors in model transformations in realistic scenarios where neither predefined patches nor behavior-safe guards such as test suites are available. Instead of using predefined patches…

Large language models have made significant progress in various language tasks, yet they still struggle with complex mathematics. In this paper, we propose ToRA a series of Tool-integrated Reasoning Agents designed to solve challenging…

Transformer models are not only successful in natural language processing (NLP) but also demonstrate high potential in computer vision (CV). Despite great advance, most of works only focus on improvement of architectures but pay little…

Fine-tuning is the primary methodology for tailoring pre-trained large language models to specific tasks. As the model's scale and the diversity of tasks expand, parameter-efficient fine-tuning methods are of paramount importance. One of…

Tool learning with foundation models aims to endow AI systems with the ability to invoke external resources -- such as APIs, computational utilities, and specialized models -- to solve complex tasks beyond the reach of standalone language…

We present a Bounded Model Checking technique for higher-order programs. The vehicle of our study is a higher-order calculus with general references. Our technique is a symbolic state syntactical translation based on SMT solvers, adapted to…