# CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

arXiv: https://arxiv.org/abs/1904.00720 · HTML: https://mlsanity.com/paper/1904.00720/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2019.

## Built on by (most cited first, 22 of 22)

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- [LLM Program Optimization via Retrieval Augmented Search](https://mlsanity.com/paper/2501.18916/) (2025)
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- [NS3: Neuro-Symbolic Semantic Code Search](https://mlsanity.com/paper/2205.10674/) (2022)
- [BERT2Code: Can Pretrained Language Models be Leveraged for Code Search?](https://mlsanity.com/paper/2104.08017/) (2021)
- [Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks](https://mlsanity.com/paper/2111.11435/) (2021)
- [LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition](https://mlsanity.com/paper/2204.09654/) (2022)
- [An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code](https://mlsanity.com/paper/2307.02443/) (2023)
- [Code Compliance Assessment as a Learning Problem](https://mlsanity.com/paper/2209.04602/) (2022)

## Abstract (arXiv)

To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called `CoaCor'), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly.