Extracting Semantics from Question-Answering Services for Snippet Reuse

Nowadays, software developers typically search online for reusable solutions to common programming problems. However, forming the question appropriately, and locating and integrating the best solution back to the code can be tricky and time consuming. As a result, several mining systems have been proposed to aid developers in the task of locating reusable snippets and integrating them into their source code. Most of these systems, however, do not model the semantics of the snippets in the context of source code provided. In this work, we propose a snippet mining system, named StackSearch, that extracts semantic information from Stack Overlow posts and recommends useful and in-context snippets to the developer. Using a hybrid language model that combines Tf-Idf and fastText, our system effectively understands the meaning of the given query and retrieves semantically similar posts. Moreover, the results are accompanied with useful metadata using a named entity recognition technique. Upon evaluating our system in a set of common programming queries, in a dataset based on post links, and against a similar tool, we argue that our approach can be useful for recommending ready-to-use snippets to the developer.