A Hidden Markov Model to detect coded information islands in free text

By Luigi Cerulo, Massimiliano Di Penta, Alberto Bacchelli, Michele Ceccarelli, Gerardo Canfora. 2013

21 citations recorded by the source corpus.

Subfields: Information Systems, Natural Language Processing, Information Extraction, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Charles Sutton
2. Miltiadis Allamanis
3. Premkumar T. Devanbu
4. Earl T. Barr
5. Michael Pradel
6. Hung Viet Pham
7. Rafael-Michael Karampatsis
8. Rishabh Singh

In Natural Language Processing

1. Premkumar T. Devanbu
2. Charles Sutton
3. Miltiadis Allamanis
4. Earl T. Barr
5. Hung Viet Pham
6. Tung Thanh Nguyen
7. Tam The Nguyen
8. Phong Minh Vu

In Information Extraction

1. Charles Sutton

In Signal Processing

1. Charles Sutton
2. Miltiadis Allamanis
3. Earl T. Barr
4. Premkumar T. Devanbu
5. Tung Thanh Nguyen
6. Eran Yahav
7. Uri Alon
8. Prem Devanbu

Citations in the corpus

• A Survey of Machine Learning for Big Code and Naturalness (2017, methodology): “[37] use a language model to detect code “islands” in free text.”
• Learning API Usages from Bytecode: A Statistical Approach (2015, methodology): “Hidden Markov Model has been used infer the next token from user-provided abbreviations [11] and detect coded information islands, such as source code, stack traces, and patches, from free tex [8].”