Mining stack overflow for API class recommendation using DOC2VEC and LDA

Document Type

Article

Publication Date

10-1-2021

Abstract

To address the lexical gaps between natural language (NL) queries and Application Programming Interface (API) documentations, and between NL queries and programme code, this study developed a novel approach for recommending Java API classes that are relevant to the program ming tasks described in NL queries. A Doc2Vec model was trained using question titles mined from Stack Overflow. The model was used to find question titles that are semantically similar to a query. Latent Dirichlet Allocation (LDA) topic modelling was applied on the Java API classes (extracted from code snippets found in the accepted answers of these similar questions) to extract a single topic comprising of the Top-10 Java API classes that are relevant to the query. The benchmarking of the proposed approach against state-of-the-art approaches, RACK and NLP2API, by using four performance metrics show that it is possible to produce comparable API recommendation results using a less complex approach that makes use of some basic machine learning models, in particular, Doc2Vec and LDA. The approach was implemented in a Java API class recommender with an Eclipse IDE's plug-in serving as the front-end.

Keywords

Application Programming Interface (API), Stack Overflow, Natural language (NL) queries

Publication Title

IET Software

Divisions

fsktm

Volume

15

Issue

5

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