Interactive, Iterative, Tooled, Rule-Based Migration of Microsoft Access
to Web Technologies
- URL: http://arxiv.org/abs/2309.03511v1
- Date: Thu, 7 Sep 2023 06:46:28 GMT
- Title: Interactive, Iterative, Tooled, Rule-Based Migration of Microsoft Access
to Web Technologies
- Authors: Santiago Bragagnolo (RMOD), Nicolas Anquetil (RMOD, CRIStAL),
St\'ephane Ducasse (CRIStAL), Abdelhak-Djamel Seriai (LIRMM/HE), Mustapha
Derras
- Abstract summary: We are working on migrating Microsoft Access monolithic applications to the web front-end and producing back-end.
To enable the developers to drive the migration to the target systems, we propose an Interactive, Iterative, Tooled, Rule-Based Migration approach.
- Score: 0.11650821883155184
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the context of a collaboration with Berger-Levrault, an IT company
producing information systems, we are working on migrating Microsoft Access
monolithic applications to the web front-end and microservices back-end. Like
in most software migrations, developers must learn the target technology, and
they will be in charge of the evolution of the migrated system in the future.
To respond to this problem, we propose the developers take over the migration
project. To enable the developers to drive the migration to the target systems,
we propose an Interactive, Iterative, Tooled, Rule-Based Migration approach.
The contributions of this article are (i) an iterative, interactive process to
language, library, GUI and architectural migration; (ii) proposal of a set of
artefacts required to support such an approach; (iii) three different
validations of the approach: (a) library and paradigm usage migration to Java
and Pharo, (b) tables and queries migration to Java and Typescript, (c) form
migration to Java Springboot and Typescript Angular.
Related papers
- GUI Agents with Foundation Models: A Comprehensive Survey [52.991688542729385]
This survey consolidates recent research on (M)LLM-based GUI agents.
We highlight key innovations in data, frameworks, and applications.
We hope this paper will inspire further developments in the field of (M)LLM-based GUI agents.
arXiv Detail & Related papers (2024-11-07T17:28:10Z) - Example-Based Automatic Migration of Continuous Integration Systems [2.2836654317217326]
Continuous Integration (CI) is a widely adopted practice for faster code change integration and testing.
Developers often migrate between CI systems in pursuit of features like matrix building or better logging.
This migration is effort intensive and error-prone owing to limited knowledge of the new CI system and its syntax.
We propose a novel approach for CI system's automatic migration: CIMig.
arXiv Detail & Related papers (2024-07-02T20:19:21Z) - Towards Modular LLMs by Building and Reusing a Library of LoRAs [64.43376695346538]
We study how to best build a library of adapters given multi-task data.
We introduce model-based clustering, MBC, a method that groups tasks based on the similarity of their adapter parameters.
To re-use the library, we present a novel zero-shot routing mechanism, Arrow, which enables dynamic selection of the most relevant adapters.
arXiv Detail & Related papers (2024-05-18T03:02:23Z) - A Generative AI Assistant to Accelerate Cloud Migration [2.9248916859490173]
The Cloud Migration LLM accepts input from the user specifying the parameters of their migration, and outputs a migration strategy with an architecture diagram.
A user study suggests that the migration LLM can assist inexperienced users in finding the right cloud migration profile, while avoiding complexities of a manual approach.
arXiv Detail & Related papers (2024-01-03T14:13:24Z) - Recommender AI Agent: Integrating Large Language Models for Interactive
Recommendations [53.76682562935373]
We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools.
InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
arXiv Detail & Related papers (2023-08-31T07:36:44Z) - LAVIS: A Library for Language-Vision Intelligence [98.88477610704938]
LAVIS is an open-source library for LAnguage-VISion research and applications.
It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets.
arXiv Detail & Related papers (2022-09-15T18:04:10Z) - Characterizing Python Library Migrations [2.2557806157585834]
We label 3,096 migration-related code changes in 335 Python library migrations.
We find that 40% of library pairs have API mappings that involve non-function program elements.
On average, a developer needs to learn about 4 APIs and 2 API mappings to perform a migration.
arXiv Detail & Related papers (2022-07-03T21:00:08Z) - Information and Communication Technology in Migration: A Framework for
Applications, Customization, and Research [1.1172382217477124]
We propose a framework for technology use based on user groups and process types.
We provide examples of using emerging technologies for migration-related tasks within the context of this framework.
arXiv Detail & Related papers (2022-04-13T19:02:42Z) - GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented
Dialogue Systems [66.92182084456809]
We introduce a novel data curation method that generates GlobalWoZ -- a large-scale multilingual ToD dataset from an English ToD dataset.
Our method is based on translating dialogue templates and filling them with local entities in the target-language countries.
We release our dataset as well as a set of strong baselines to encourage research on learning multilingual ToD systems for real use cases.
arXiv Detail & Related papers (2021-10-14T19:33:04Z) - Migratable AI: Personalizing Dialog Conversations with migration context [25.029958885340058]
We collected a dataset from the dialog conversations between crowdsourced workers with the migration context.
We trained the generative and information retrieval models on the dataset using with and without migration context.
We believe that the migration dataset would be useful for training future migratable AI systems.
arXiv Detail & Related papers (2020-10-22T22:23:03Z) - MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer [136.09386219006123]
We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages.
MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning.
arXiv Detail & Related papers (2020-04-30T18:54:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.