This paper describes an emergent collaborative project between the Universities of Reunion (Institut de Recherches en Mathematiques et Informatique Appliquees - IREMIA) and Mauritius (Virtual Centre for Innovative Learning Technologies - VCILT) to make scientific research results in insular tropical environment available to a wider audience, create awareness and contribute to a better protection of this environment. On the long term, the project also aims at developing a strong regional competency kernel through the use of technology as a tool to encourage the development, documentation and use of indigenous information and knowledge about tropical islands environment: evaluate what is available, how it can be preserved, strengthened, amplified or improved through the introduction of ICT and how it could participate, in the basket of globalisation, as our share for a common identity. The authors show how enhancement and dissemination of research results can be achieved by a combination of an upstream strategy based on IKBS, a generic software for building knowledge bases on the Internet in natural sciences, and a downstream one, based on a pedagogical approach borrowed from the DARWIN world. The final outcome expected for the overall IKBS-DARWIN project is a transversal bilingual (French-English) research and educational portal, supporting enhancement online activities in several related domains at several levels of competency, from researchers and graduate students to undergraduate students, associations or communities of practice.

Predicting stability in object-oriented (OO) software, i.e., the ease with which a software item can evolve while preserving its design, is a key feature for software maintenance. We present a novel approach which relies on the case-based reasoning (CBR) paradigm. Thus, to predict the chances of an OO software item breaking downward compatibility, our method uses knowledge of past evolution extracted from different software versions. A comparison of our similarity-based approach to a classical inductive method such as decision trees, is presented which includes various tests on large datasets from existing software.