Dynamic inference of static types for ruby

By Jong-hoon An, Avik Chaudhuri, Jeffrey S. Foster, Michael W. Hicks. 2011

97 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Software, 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 Artificial Intelligence

1. Arjun Guha
2. Milod Kazerounian
3. Jeffrey S. Foster
4. Georgios Gousios
5. Kexin Pei
6. Michael Pradel
7. Carolyn Jane Anderson
8. Luna Phipps-Costin

In Information Systems

1. Arjun Guha
2. Georgios Gousios
3. Milod Kazerounian
4. Jeffrey S. Foster
5. Carolyn Jane Anderson
6. Luna Phipps-Costin
7. Michael Greenberg
8. Satish Chandra

In Software

1. Georgios Gousios
2. Michael Pradel
3. Arjun Guha
4. Kexin Pei
5. Jibesh Patra
6. X. Zhang
7. Baowen Xu
8. Lin Chen

In Program Analysis & Verification

1. Arjun Guha
2. Milod Kazerounian
3. Jeffrey S. Foster
4. Georgios Gousios
5. Carolyn Jane Anderson
6. Luna Phipps-Costin
7. Michael Greenberg
8. Satish Chandra

Citations in the corpus

• TypeWriter: neural type prediction with search-based validation (2019, background): “Dynamic type inference [3, 30] tracks data flows between functions, e.”
• The ruby type checker (2013, methodology): “Rtc is implemented in a similar fashion to Rubydust.”
• The ins and outs of gradual type inference (2012, background): “Recent work has explored dynamic techniques for type inference and type specialization [3, 10] for dynamic languages.”
• NL2Type: Inferring JavaScript Function Types from Natural Language Information (2019, background): “Previous work has addressed the type inference problem through static analyses of code [5]–[8].”
• Learning to Predict User-Defined Types (2023, unknown): “Type Inference Dynamic type inference techniques [58], [59] and type checkers [60], [61], [62], [63] achieve soundness by enforcing type constraints.”
• Solver-based gradual type migration (2021, methodology): “However, its type system is significantly different from the GTLC, which hinders comparisons to contemporary type migration tools for the GTLC. Flanagan [1997, p. 41]’s discussion of how Soft Scheme’s sophistication can lead to un-intuitive types inspired work on set-based analysis of Scheme program”
• Python probabilistic type inference with natural language support (2016, methodology): “There have been a lot of works on type inference for dynamic languages in general [26, 28, 23, 24].”
• Sound, heuristic type annotation inference for Ruby (2020, background): “[2] describe a type inference system for Ruby that infers that a certain position accepts any object with >, <<, >>, &, and ˆ methods.”