# TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search

arXiv: https://arxiv.org/abs/2003.03238 · HTML: https://mlsanity.com/paper/2003.03238/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [On the Importance of Building High-quality Training Datasets for Neural Code Search](https://mlsanity.com/paper/2202.06649/) (2022)
- [Exploring Continual Learning for Code Generation Models](https://mlsanity.com/paper/2307.02435/) (2023)
- [An Exploratory Study on Code Attention in BERT](https://mlsanity.com/paper/2204.10200/) (2022)
- [CoSQA+: Pioneering the Multi-Choice Code Search Benchmark with Test-Driven Agents](https://mlsanity.com/paper/2406.11589/) (2024)
- [API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations](https://mlsanity.com/paper/2103.10668/) (2021)
- [RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command Explainer](https://mlsanity.com/paper/2409.02074/) (2024)
- [Can Developers Prompt? A Controlled Experiment for Code Documentation Generation](https://mlsanity.com/paper/2408.00686/) (2024)
- [On the Compression of Language Models for Code: An Empirical Study on CodeBERT](https://mlsanity.com/paper/2412.13737/) (2024)
- [FACOS: Finding API Relevant Contents on Stack Overflow with Semantic and Syntactic Analysis](https://mlsanity.com/paper/2111.07238/) (2021)

## Abstract (arXiv)

Code summarization and code search have been widely adopted in sofwaredevelopmentandmaintenance. However, fewstudieshave explored the efcacy of unifying them. In this paper, we propose TranS^3 , a transformer-based framework to integrate code summarization with code search. Specifcally, for code summarization,TranS^3 enables an actor-critic network, where in the actor network, we encode the collected code snippets via transformer- and tree-transformer-based encoder and decode the given code snippet to generate its comment. Meanwhile, we iteratively tune the actor network via the feedback from the critic network for enhancing the quality of the generated comments. Furthermore, we import the generated comments to code search for enhancing its accuracy. To evaluatetheefectivenessof TranS^3 , we conduct a set of experimental studies and case studies where the experimental results suggest that TranS^3 can signifcantly outperform multiple state-of-the-art approaches in both code summarization and code search and the study results further strengthen the efcacy of TranS^3 from the developers' points of view.