Software development using context aware searching of components in large repositories

This paper proposes a new approach to locate software components from large component online open source repositories which encompasses the inherent features of context-aware browsing, ranking and semantic tagging. Tagging of individual components helps making search fast and efficient. We are trying to improvise the results of context aware browsing by ranking them on the basis of Hidden Markov Models. The inputs to Hidden Markov Models consists of auto generated contextual queries. These queries formulate the resource set of our Hidden Markov model. The queries are ameliorated using reformulation, specialization, generalization and general association. This automation not only reduces the search space of components for an efficient browsing but also it enables developers to use those components whose existence they do not even prognosticate.