Parameter-Free Probabilistic API Mining at GitHub Scale

By Jaroslav M. Fowkes, Charles Sutton. 2015

13 citations recorded by the source corpus.

Subfields: Evaluation & Benchmarks

How researchers see this paper

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

In Evaluation & Benchmarks

1. Miltiadis Allamanis
2. Charles Sutton
3. Earl T. Barr
4. Dongmei Zhang
5. Hongyu Zhang
6. Premkumar T. Devanbu
7. Tien Nhut Nguyen
8. Anh Tuan Nguyen

Citations in the corpus

• A Survey of Machine Learning for Big Code and Naturalness (2017, methodology): “sentation (c) Representation (g) Application Allamanis and Sutton [12]∗ Syntax Graphical Model Idiom Mining Allamanis et al. [4] Abstracted AST Graphical Model Semantic Idiom Mining Fowkes and Sutton [63] API Call Sequences Graphical Model API Mining Murali et al. [135] Sketch Synthesis Graphical Mo”
• Exploring API Embedding for API Usages and Applications (2017, methodology): “PAM [24] is a parameter-free, probabilistic algorithm to mine API patterns.”
• Deep API learning (2016, methodology): “parse Yet learning the APIs of an unfamiliar library or software framework can be a significant obstacle for developers [15, 46].”