Learning to rank code examples for code search engines
By Haoran Niu, Iman Keivanloo, Ying Zou. 2017
81 citations recorded by the source corpus.
Subfields: Computer Science Applications, Information Systems, Information Retrieval & Search
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
- Premkumar T. Devanbu
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Bogdan Vasilescu
- Graham Neubig
- Sunghun Kim
- Hongyu Zhang
In Information Systems
- Miltiadis Allamanis
- Charles Sutton
- Earl T. Barr
- Graham Neubig
- Bogdan Vasilescu
- Premkumar T. Devanbu
- Hongyu Zhang
- Michael Pradel
In Information Retrieval & Search
- Miltiadis Allamanis
- Graham Neubig
- Bogdan Vasilescu
- Hongyu Zhang
- Yuting Chen
- Lingxiao Jiang
- Hang Yu
- Charles Sutton
Citations in the corpus
- A Survey of Machine Learning for Big Code and Naturalness (2017, methodology): “[145] has used learning-to-rank methods but with manually extracted features.”
- Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow (2018, methodology): “Some work on code search has been performed by retrieving candidate code snippets given an intent based on weighted keyword matches and other features [29, 42].”
- Are the Code Snippets What We Are Searching for? A Benchmark and an Empirical Study on Code Search with Natural-Language Queries (2020, methodology): “Niu et al. [48] have used learning-to-rank methods to automatically train a ranking schema.”
- Deep Code Search (2018, background): “In future work, more code features (such as programming context) [58] could be considered in our model to further adjust the results.”