# Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning

arXiv: https://arxiv.org/abs/2002.10198 · HTML: https://mlsanity.com/paper/2002.10198/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [On the Evaluation of Neural Code Summarization](https://mlsanity.com/paper/2107.07112/) (2021)
- [On the Importance of Building High-quality Training Datasets for Neural Code Search](https://mlsanity.com/paper/2202.06649/) (2022)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent](https://mlsanity.com/paper/2406.00215/) (2024)
- [CoCoSoDa: Effective Contrastive Learning for Code Search](https://mlsanity.com/paper/2204.03293/) (2022)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Source Code Summarization with Structural Relative Position Guided Transformer](https://mlsanity.com/paper/2202.06521/) (2022)
- [A Topic-aware Summarization Framework with Different Modal Side Information](https://mlsanity.com/paper/2305.11503/) (2023)
- [Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach](https://mlsanity.com/paper/2103.11448/) (2021)
- [Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code](https://mlsanity.com/paper/2308.13507/) (2023)
- [A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective](https://mlsanity.com/paper/2202.04847/) (2022)
- [NS3: Neuro-Symbolic Semantic Code Search](https://mlsanity.com/paper/2205.10674/) (2022)
- [Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents](https://mlsanity.com/paper/2406.12806/) (2024)
- [Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search](https://mlsanity.com/paper/2107.04773/) (2021)
- [Selective Shot Learning for Code Explanation](https://mlsanity.com/paper/2412.12852/) (2024)
- [Cone: Unsupervised Contrastive Opinion Extraction](https://mlsanity.com/paper/2305.04599/) (2023)

## Abstract (arXiv)

Code summarization generates brief natural language description given a source code snippet, while code retrieval fetches relevant source code given a natural language query. Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance. However, researchers have yet been able to effectively leverage the intrinsic connection between the two tasks as they train these tasks in a separate or pipeline manner, which means their performance can not be well balanced. In this paper, we propose a novel end-to-end model for the two tasks by introducing an additional code generation task. More specifically, we explicitly exploit the probabilistic correlation between code summarization and code generation with dual learning, and utilize the two encoders for code summarization and code generation to train the code retrieval task via multi-task learning. We have carried out extensive experiments on an existing dataset of SQL and Python, and results show that our model can significantly improve the results of the code retrieval task over the-state-of-art models, as well as achieve competitive performance in terms of BLEU score for the code summarization task.