KEYWORD BASED CONTEXTUAL DEPENDENCY GRAPH MODEL FOR SOURCE CODE TO API DOCUMENTATION MAPPING

摘要

Natural language processing on software systems usually contain high dimensional noisy and irrelevant features which lead to inaccurate and poor contextual similarity between the project source code and its API documentation. Most of the traditional source code analysis models are independent of finding and extracting the relevant features for contextual similarity. As the size of the project source code and its related API documentation increases, these models incorporate the contextual similarity between the source code and API documentation for code analysis. One of the best solutions for this problem is finding the essential features using the source code dependency graph. In this paper, the dependency graph is used to compute the contextual similarity computation between the source code metrics and its API documents. A novel contextual similarity measure is used to find the relationship between the project source code metrics to the API documents. Proposed model is evaluated on different project source codes and API documents in terms of pre-processing, context similarity and runtime. Experimental results show that the proposed model has high computational efficiency compared to the existing models on the large size datasets.