Name
Abstract | ||
|---|---|---|
Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the code in a natural language such as English. In spite of its usefulness, most source code does not have corresponding pseudo-code because it is tedious to create. This paper demonstrates a tool Pseudogen that makes it possible to automatically generate pseudo-code from source code using statistical machine translation (SMT). Pseudogen currently supports generation of English or Japanese pseudo-code from Python source code, and the SMT framework makes it easy for users to create new generators for their preferred source code/pseudo-code pairs. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ASE.2015.107 | Automated Software Engineering |
Keywords | Field | DocType |
machine translation,programming language,natural language | Write-only language,Programming language,Source code,Computer science,Compiler,Code generation,Assembly language,Low-level programming language,Programming language implementation,Computer programming | Conference |
ISSN | Citations | PageRank |
1527-1366 | 2 | 0.37 |
References | Authors | |
18 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroyuki Fudaba | 1 | 47 | 1.75 |
| Yusuke Oda | 2 | 135 | 7.69 |
| Koichi Akabe | 3 | 2 | 1.04 |
| Graham Neubig | 4 | 989 | 130.31 |
| Hideaki Hata | 5 | 273 | 28.18 |
| Sakriani Sakti | 6 | 257 | 65.02 |
| Tomoki Toda | 7 | 1874 | 167.18 |
| Satoshi Nakamura | 8 | 1099 | 194.59 |