Spärck: Information retrieval system of machine learning good practices for software engineering
In this project, we propose a tool for the developers to search for good machine learning (ML) practices appropriate for the software engineering (SE) assignments they are working on. We expect this tool makes ML good practices easily accessible and promotes their use. For this, we defined a structu...
- Autores:
-
Cabra Acela, Laura Helena
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/64399
- Acceso en línea:
- http://hdl.handle.net/1992/64399
- Palabra clave:
- Machine learning
Information retrieval
Good practices
Software engineering
Ingeniería
- Rights
- openAccess
- License
- Atribución-CompartirIgual 4.0 Internacional
| Summary: | In this project, we propose a tool for the developers to search for good machine learning (ML) practices appropriate for the software engineering (SE) assignments they are working on. We expect this tool makes ML good practices easily accessible and promotes their use. For this, we defined a structure that described the relationships between stages of the ML pipeline, tasks, and good practices. Moreover, we implemented and validated an information retrieval (IR) model for the good practices gathered. Furthermore, we developed and validated a platform that allows users to search for good practices in ML for SE. This platform includes three main features: (i) a search bar that uses the implemented IR model. (ii) a tool to filter the practices by tasks. (iii) an interactive tool that classifies the information by the relationship between stages, tasks, and practices. |
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