Paper Info
Title
CodeMend: Assisting Interactive Programming with Bimodal Embedding.
Abstract
Software APIs often contain too many methods and parameters for developers to memorize or navigate effectively. Instead, developers resort to finding answers through online search engines and systems such as Stack Overflow. However, the process of finding and integrating a working solution is often very time-consuming. Though code search engines have increased in quality, there remain significant language- and workflow-gaps in meeting end-user needs. Novice and intermediate programmers often lack the language to query, and the expertise in transferring found code to their task. To address this problem, we present CodeMend, a system to support finding and integration of code. CodeMend leverages a neural embedding model to jointly model natural language and code as mined from large Web and code datasets. We also demonstrate a novel, mixed-initiative, interface to support query and integration steps. Through CodeMend, end-users describe their goal in natural language. The system makes salient the relevant API functions, the lines in the end-user's program that should be changed, as well as proposing the actual change. We demonstrate the utility and accuracy of CodeMend through lab and simulation studies.
Year
DOI
Venue
2016
10.1145/2984511.2984544
UIST
Keywords
Field
DocType
Natural language code search, word embedding, program embedding
Embedding,Computer science,Source code,Code generation,Software,Natural language,Human–computer interaction,Word embedding,Interactive programming,Online search
Conference
Citations 
PageRank 
References 
7
0.42
36
Authors
5
Name
Order
Citations
PageRank
Xin Rong1332.63
Shiyan Yan2172.29
Stephen Oney328513.12
Mira Dontcheva4148785.48
Eytan Adar52648281.27