Bayesian specification learning for finding API usage errors

By Vijayaraghavan Murali, Swarat Chaudhuri, C. Jermaine. 2017

47 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software, Probabilistic & Bayesian Methods, Program Analysis & Verification

How researchers see this paper

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

In Computer Networks and Communications

1. Earl T. Barr
2. Charles Sutton
3. Premkumar T. Devanbu
4. Miltiadis Allamanis
5. Uri Alon
6. Eran Yahav

In Information Systems

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

In Software

1. Charles Sutton
2. Miltiadis Allamanis
3. Earl T. Barr
4. Premkumar T. Devanbu
5. Michael Pradel
6. Rishabh Singh
7. Marc Brockschmidt
8. Sahil Bhatia

In Probabilistic & Bayesian Methods

1. Premkumar T. Devanbu
2. Earl T. Barr
3. Miltiadis Allamanis
4. Charles Sutton
5. Pedro M. Domingos
6. Hoifung Poon
7. Yoshua Bengio

In Program Analysis & Verification

1. Miltiadis Allamanis
2. Charles Sutton
3. Earl T. Barr
4. Premkumar T. Devanbu
5. Eran Yahav
6. Uri Alon
7. Marc Brockschmidt
8. Omer Levy

Citations in the corpus

• A Survey of Machine Learning for Big Code and Naturalness (2017, methodology): “[136] use a combination of topic models to bias a recurrent neural network that models the sequences of API calls in a learned probabilistic automaton.”