Paper Info
Title
T2API: synthesizing API code usage templates from English texts with statistical translation.
Abstract
In this work, we develop T2API, a statistical machine translation-based tool that takes a given English description of a programming task as a query, and synthesizes the API usage template for the task by learning from training data. T2API works in two steps. First, it derives the API elements relevant to the task described in the input by statistically learning from a StackOverflow corpus of text descriptions and corresponding code. To infer those API elements, it also considers the context of the words in the textual input and the context of API elements that often go together in the corpus. The inferred API elements with their relevance scores are ensembled into an API usage by our novel API usage synthesis algorithm that learns the API usages from a large code corpus via a graph-based language model. Importantly, T2API is capable of generating new API usages from smaller, previously-seen usages.
Year
DOI
Venue
2016
10.1145/2950290.2983931
SIGSOFT FSE
Keywords
Field
DocType
Text-to-Code Translation,API Usage Synthesis,Graph-based Statistical Machine Translation
Training set,Graph,Programming language,Computer science,Machine translation,Artificial intelligence,Natural language processing,Template,Language model
Conference
Citations 
PageRank 
References 
12
0.53
24
Authors
5
Name
Order
Citations
PageRank
Thanh Nguyen1245.76
Peter C. Rigby268235.28
Anh Tuan Nguyen383335.34
Mark Karanfil4150.90
Tien N. Nguyen51988100.15