# Deep API Learning

arXiv: https://arxiv.org/abs/1605.08535 · HTML: https://mlsanity.com/paper/1605.08535/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2016.

## Built on by (most cited first, 30 of 41)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Retrieval Augmented Code Generation and Summarization](https://mlsanity.com/paper/2108.11601/) (2021)
- [ReACC: A Retrieval-Augmented Code Completion Framework](https://mlsanity.com/paper/2203.07722/) (2022)
- [When Language Model Meets Private Library](https://mlsanity.com/paper/2210.17236/) (2022)
- [CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2104.02443/) (2021)
- [Understanding Neural Code Intelligence Through Program Simplification](https://mlsanity.com/paper/2106.03353/) (2021)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [On the Evaluation of Commit Message Generation Models: An Experimental Study](https://mlsanity.com/paper/2107.05373/) (2021)
- [Memorization and Generalization in Neural Code Intelligence Models](https://mlsanity.com/paper/2106.08704/) (2021)
- [GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search](https://mlsanity.com/paper/2111.02671/) (2021)
- [Code Search based on Context-aware Code Translation](https://mlsanity.com/paper/2202.08029/) (2022)
- [Cross-Language Binary-Source Code Matching with Intermediate Representations](https://mlsanity.com/paper/2201.07420/) (2022)
- [Compositional API Recommendation for Library-Oriented Code Generation](https://mlsanity.com/paper/2402.19431/) (2024)
- [On the Effectiveness of Pretrained Models for API Learning](https://mlsanity.com/paper/2204.03498/) (2022)
- [CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale](https://mlsanity.com/paper/2502.16645/) (2025)
- [Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study](https://mlsanity.com/paper/2305.03017/) (2023)
- [Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models](https://mlsanity.com/paper/2106.13928/) (2021)
- [Revisiting and Improving Retrieval-Augmented Deep Assertion Generation](https://mlsanity.com/paper/2309.10264/) (2023)
- [Impact-driven Context Filtering For Cross-file Code Completion](https://mlsanity.com/paper/2508.05970/) (2025)
- [Natural Language-Guided Programming](https://mlsanity.com/paper/2108.05198/) (2021)
- [deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search](https://mlsanity.com/paper/2103.13020/) (2021)
- [Unsupervised Learning of General-Purpose Embeddings for Code Changes](https://mlsanity.com/paper/2106.02087/) (2021)
- [Test2Vec: An Execution Trace Embedding for Test Case Prioritization](https://mlsanity.com/paper/2206.15428/) (2022)
- [PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis](https://mlsanity.com/paper/2306.00210/) (2023)
- [Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?](https://mlsanity.com/paper/2309.07804/) (2023)
- [Structured Code Representations Enable Data-Efficient Adaptation of Code Language Models](https://mlsanity.com/paper/2401.10716/) (2024)
- [COMET: Generating Commit Messages using Delta Graph Context Representation](https://mlsanity.com/paper/2402.01841/) (2024)
- [BERT2Code: Can Pretrained Language Models be Leveraged for Code Search?](https://mlsanity.com/paper/2104.08017/) (2021)
- [Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables](https://mlsanity.com/paper/2209.01566/) (2022)
- [GitHub Considered Harmful? Analyzing Open-Source Projects for the Automatic Generation of Cryptographic API Call Sequences](https://mlsanity.com/paper/2211.13498/) (2022)

## Abstract (arXiv)

Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences. These approaches treat queries and APIs as bag-of-words (i.e., keyword matching or word-to-word alignment) and lack a deep understanding of the semantics of the query. We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query. Instead of a bags-of-words assumption, it learns the sequence of words in a query and the sequence of associated APIs. DeepAPI adapts a neural language model named RNN Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length context vector, and generates an API sequence based on the context vector. We also augment the RNN Encoder-Decoder by considering the importance of individual APIs. We empirically evaluate our approach with more than 7 million annotated code snippets collected from GitHub. The results show that our approach generates largely accurate API sequences and outperforms the related approaches.