Code search with natural language terms performs poorly because programming concepts do not always lexically match their syntactic forms. For example, in Java, the programming concept "array" does not match with its syntactic representation of "[ ]". Code search engines can assist developers more effectively over natural language queries if such mappings existed for a variety of programming languages. In this work, we present a programming language agnostic technique to discover such mappings between syntactic forms and natural language terms representing programming concepts. We use the questions and answers in Stack Overflow to create this mapping. We implement our approach in a tool called ANNE. To evaluate its effectiveness, we conduct a user study in an academic setting in which teaching assistants use ANNE to search for code snippets in student submissions. With the use of ANNE, we find that the participants are 29% quicker with no significant drop in correctness and completeness.

Formation of quizzes is a vital problem as they are an important part of learning. To create a quiz on a particular topic, its related terms need to be identified for further use in extraction of questions on the topic. These terms are referred to as entities for the topic and the task of distinguishing entities from general purpose terms is termed entity discovery. We know that discussion forums and question-answer sites on software contain questions using programming terms in their posts. In this work, we mine patterns in user queries from such a forum and then automatically discover entities for programming languages using these patterns. We use these entities to extract questions related to the programming language and form automated quizzes using them.