recommending api methods based on identifier contexts
Reuse recommendation systems suggest functions or code snippets that are useful for the programming task at hand within the IDE. These systems utilize different aspects from the context of the cursor position within the source file being edited for inferring which functionality is needed next. Current approaches are based on structural information like inheritance relations or type/method usages. We propose a novel method that utilizes the knowledge embodied in the identifiers as a basis for the recommendation of API methods. This approach has the advantage that relevant recommendations can also be made in cases where no methods are called in the context or if contexts use distinct but semantically similar types or methods. First experiments show, that the correct method is recommended in about one quarter to one third of the cases.
recommendation system software reuse