Exploring API Embedding for API Usages and Applications

By Trong Duc Nguyen, Anh Tuan Nguyen, Hung Dang Phan, Tien Nhut Nguyen. 2017

172 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems, Information Retrieval & Search, Natural Language Processing

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. Charles Sutton
2. Miltiadis Allamanis
3. Stephan Günnemann
4. Earl T. Barr
5. Michael Pradel
6. Daniel Zügner
7. Premkumar T. Devanbu
8. Jure Leskovec

In Computer Networks and Communications

1. Sunghun Kim
2. Earl T. Barr
3. Charles Sutton
4. Hongyu Zhang
5. Daniel Zügner
6. Michael Pradel
7. Premkumar T. Devanbu
8. Koushik Sen

In Information Systems

1. Miltiadis Allamanis
2. Charles Sutton
3. Michael Pradel
4. Earl T. Barr
5. Koushik Sen
6. Premkumar T. Devanbu
7. Hongyu Zhang
8. Rafael-Michael Karampatsis

In Information Retrieval & Search

1. Miltiadis Allamanis
2. Hongyu Zhang
3. Daniel Zügner
4. Jure Leskovec
5. Artur Andrzejak
6. Koushik Sen
7. Michael Pradel
8. Lutz Büch

In Natural Language Processing

1. Charles Sutton
2. Miltiadis Allamanis
3. Michael Pradel
4. Premkumar T. Devanbu
5. Earl T. Barr
6. Koushik Sen
7. Michele Catasta
8. Jure Leskovec

Citations in the corpus

• A Survey of Machine Learning for Big Code and Naturalness (2017, background): “thesis Graphical Model Sketch Mining Murali et al. [136] API Usage Errors Graphical Model Defect Prediction Movshovitz-Attias and Cohen [134] Tokens Graphical Model Knowledge-BaseMining Nguyen et al. [143] API Usage Distributed API Mining Fowkes et al. [62] Tokens Graphical Model Code Summarization ”
• DeepBugs: a learning approach to name-based bug detection (2018, methodology): “Other recent work has proposed embeddings for source code, e.g., for API methods [Nguyen et al. 2017], for terms that occur both in programs and natural language documentation [Ye et al. 2016], as well as a generic, AST-based representation of code [Alon et al. 2018].”
• Deep Learning to Find Bugs (2017, methodology): “, for API methods [29], or for terms that occur both in programs and natural language documentation [45].”
• Learning-Based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection (2019, methodology): “Researchers have also investigated the semantics captured in these embeddings, either by vector arithmetic yielding word analogies [19], [20] (as in the follow-up paper to word2vec method [27]), or by plotting a neighbourhood-preserving 2D projection [23].”
• Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts (2017, background): “ne learning-based natural language processing, where they abstract words into vectors [25]. Beyond pure natural language texts, embeddings for code elements have been proposed, e.g., for API elements [27] and for terms that appear both in code and in natural language documentation [41]. Our embeddin”
• Deep Code Search (2018, background): “Recently, researchers have investigated possible applications of deep learning techniques to source code [7, 38, 53, 56, 60, 64, 75, 76].”
• Language-Agnostic Representation Learning of Source Code from Structure and Context (2021, background): “These representations—and their variants like (Mou et al., 2016)—can then be used to predict sequences of identifier sub-tokens (Allamanis et al., 2015) or API calls (Acharya et al., 2007; Nguyen et al., 2017).”