-
Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics
Software developers frequently issue generic natural language queries fo...
read it
-
Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions
Translating verbose information needs into crisp search queries is a phe...
read it
-
QUICKAR: Automatic Query Reformulation for Concept Location using Crowdsourced Knowledge
During maintenance, software developers deal with numerous change reques...
read it
-
Generating Question Titles for Stack Overflow from Mined Code Snippets
Stack Overflow has been heavily used by software developers as a popular...
read it
-
Recommending Comprehensive Solutions for Programming Tasks by Mining Crowd Knowledge
Developers often search for relevant code examples on the web for their ...
read it
-
Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding
Developers increasingly rely on text matching tools to analyze the relat...
read it
-
Exploring task-based query expansion at the TREC-COVID track
We explore how to generate effective queries based on search tasks. Our ...
read it
Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow
As a popular Q A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning. With query logs provided by Stack Overflow, we construct a large-scale query reformulation corpus, including the original queries and corresponding reformulated ones. Our approach trains a Transformer model that can automatically generate candidate reformulated queries when given the user's original query. The evaluation results show that our approach outperforms five state-of-the-art baselines, and achieves a 5.6 πΈπ₯πππ‘πππ‘πβ and a 4.8
READ FULL TEXT
Comments
There are no comments yet.