HiRec: API Recommendation using Hierarchical Context

Abstract

Context-aware API recommendation techniques aim to generate a ranked list of candidate APIs on an editing position during development. The basic context used in traditional API recommendation mainly focuses on the APIs from third-party libraries, limit or even ignore the usage of project-specific code. The limited usage of project-specific code may result in the lack of context information, and degrade the effectiveness of API recommendation. To address this problem, we introduce a novel type of context, i.e., hierarchical context, which can leverage the hidden information of project-specific code by analyzing the call graph. In hierarchical context, a project-specific API is presented as a sequence of low-leveled APIs from third-party libraries. We propose an approach, i.e., HiRec, which builds on the basis of hierarchical context. HiRec is evaluated on 108 projects and the results show that HiRec can obtain much more accurate results than all the other selected approaches in terms of top-5 and top-10 accuracy due to the strong ability of context representation. And HiRec performs closely to the outstanding tools in terms of top-1 accuracy. The average time of recommending execution is less than 1 seconds in most cases, which is acceptable for interaction in an IDE. Unlike current approaches, the effectiveness of HiRec is not impacted much by editing positions. And we can obtain more accurate results from HiRec with larger sizes of training data and hierarchical context.