Published

2014-01-01

CodeRAnts: A recommendation method based on collaborative searching and ant colonies, applied to reusing of open source code

Coderants: método de recomendación basado en la búsqueda colaborativa y las colonias de hormigas. Aplicado a la reutilización del código fuente abierto

Keywords:

Recommender Systems on Software Engineering, recommendation method based on collaborative searching, software reuse, open source software, ant colony (en)
Sistemas de recomendación para ingeniería de software, método de recomendación basado en la búsqueda colaborativa, reutilización de software, software de fuente abierta y colonia de hormigas (es)

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Authors

  • Isaac Caicedo-Castro University of Córdoba
  • Helga Duarte-Amaya Universidad National de Colombia

This paper presents CodeRAnts, a new recommendation method based on a collaborative searching technique and inspired on the ant colony metaphor. This method aims to fill the gap in the current state of the matter regarding recommender systems for software reuse, for which prior works present two problems. The first is that, recommender systems based on these works cannot learn from the collaboration of programmers and second, outcomes of assessments carried out on these systems present low precision measures and recall and in some of these systems, these metrics have not been evaluated. The work presented in this paper contributes a recommendation method, which solves these problems.

Este artículo presenta CodeRAnts: un nuevo método de recomendación basado en la técnica de búsqueda colaborativa e inspirada en la metáfora de la colonia de hormigas. Este método es propuesto con el objetivo de llenar el vacío en el estado del arte en cuanto a los sistemas de recomendación diseñados para reutilizar software, cuyos trabajos previos presentan dos problemas. El primero, es que los sistemas de recomendación basados en esos trabajos no pueden aprender de la colaboración de los programadores, y segundo, que los resultados de las pruebas realizados sobre estos sistemas presentan medidas bajas de precisión y remembranza, incluso, en algunos de estos sistemas no se hizo una evaluación de estas métricas. La contribución de este trabajo es un método de recomendación que resuelva dichos problemas.

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