Over the ten years a community on context has emerged. Brzillon (1999) proposed a survey of the literature about context in artificial intelligence. There is a now series of conference on context, a web site and a mailing list. The number of web pages with the word "context" has been multiply by four in the last five years. Being among the instigators of the use of context in real-world applications, we present in this paper the evolution of our thoughts over the last years and the result that is obtained as a representation formalism based on contextual graphs and used in a real-world application called SART. We also point out some similarities between contextual graphs and some models developed in other domains.

The paper presents a similarity-based retrieval framework for a software repository that aids the process of maintaining, understanding, and migrating legacy software systems [12]. Designing a software repository involves three issues: (1) information content; (2) information representation; and (3) strategies for accessing repository artifacts. Assuming the architecture presented in [12] we extend the retrieval system to support imprecise queries, iterative browsing, and diverse users. Because of repository size, complexity of queries and relations among artifacts, we take a performance approach to support a scalable implementation. We propose a retrieval system that uses numeric [31] and semantically rich context-based similarity [19]. Efficient iterative browsing is based on an incremental query evaluation algorithm from database management systems [20]. Explicitly defined context supports various retrieval strategies and diverse user models. 1. Introduction Software repositories ...