# Code Search based on Context-aware Code Translation

arXiv: https://arxiv.org/abs/2202.08029 · HTML: https://mlsanity.com/paper/2202.08029/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 72 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks](https://mlsanity.com/paper/2102.02017/) (2021)
- [Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval](https://mlsanity.com/paper/1502.06922/) (2015)
- [Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations](https://mlsanity.com/paper/2009.02731/) (2020)
- [When Deep Learning Met Code Search](https://mlsanity.com/paper/1905.03813/) (2019)
- [Re-evaluating Automatic Metrics for Image Captioning](https://mlsanity.com/paper/1612.07600/) (2016)
- [Deep API Learning](https://mlsanity.com/paper/1605.08535/) (2016)
- [Multi-Modal Attention Network Learning for Semantic Source Code Retrieval](https://mlsanity.com/paper/1909.13516/) (2019)
- [CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning](https://mlsanity.com/paper/1904.00720/) (2019)
- [Opportunities and Challenges in Code Search Tools](https://mlsanity.com/paper/2011.02297/) (2020)
- [A Multi-Perspective Architecture for Semantic Code Search](https://mlsanity.com/paper/2005.06980/) (2020)
- [Practical Program Repair via Bytecode Mutation](https://mlsanity.com/paper/1807.03512/) (2018)
- [Query Expansion Based on Crowd Knowledge for Code Search](https://mlsanity.com/paper/1703.01443/) (2017)
- [OCoR: An Overlapping-Aware Code Retriever](https://mlsanity.com/paper/2008.05201/) (2020)
- [Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics](https://mlsanity.com/paper/1807.08798/) (2018)
- [Learning API Usages from Bytecode: A Statistical Approach](https://mlsanity.com/paper/1507.07306/) (2015)
- [deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search](https://mlsanity.com/paper/2103.13020/) (2021)
- [JCoffee: Using Compiler Feedback to Make Partial Code Snippets Compilable](https://mlsanity.com/paper/2009.05090/) (2020)

## Built on by (most cited first, 14 of 14)

- [An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection](https://mlsanity.com/paper/2406.06822/) (2024)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Backdooring Neural Code Search](https://mlsanity.com/paper/2305.17506/) (2023)
- [An Extractive-and-Abstractive Framework for Source Code Summarization](https://mlsanity.com/paper/2206.07245/) (2022)
- [Rethinking Negative Pairs in Code Search](https://mlsanity.com/paper/2310.08069/) (2023)
- [Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness](https://mlsanity.com/paper/2502.15830/) (2025)
- [LExecutor: Learning-Guided Execution](https://mlsanity.com/paper/2302.02343/) (2023)
- [Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?](https://mlsanity.com/paper/2312.00413/) (2023)
- [Eliminating Backdoors in Neural Code Models for Secure Code Understanding](https://mlsanity.com/paper/2408.04683/) (2024)
- [A Joint Learning Model with Variational Interaction for Multilingual Program Translation](https://mlsanity.com/paper/2408.14515/) (2024)
- [BadCS: A Backdoor Attack Framework for Code search](https://mlsanity.com/paper/2305.05503/) (2023)
- [MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://mlsanity.com/paper/2203.14093/) (2022)
- [MT4DP: Data Poisoning Attack Detection for DL-based Code Search Models via Metamorphic Testing](https://mlsanity.com/paper/2507.11092/) (2025)
- [Securing Code Understanding: Detecting Natural Backdoor Vulnerability in Code Language Models](https://mlsanity.com/paper/2606.10846/) (2026)

## Abstract (arXiv)

Code search is a widely used technique by developers during software development. It provides semantically similar implementations from a large code corpus to developers based on their queries. Existing techniques leverage deep learning models to construct embedding representations for code snippets and queries, respectively. Features such as abstract syntactic trees, control flow graphs, etc., are commonly employed for representing the semantics of code snippets. However, the same structure of these features does not necessarily denote the same semantics of code snippets, and vice versa. In addition, these techniques utilize multiple different word mapping functions that map query words/code tokens to embedding representations. This causes diverged embeddings of the same word/token in queries and code snippets. We propose a novel context-aware code translation technique that translates code snippets into natural language descriptions (called translations). The code translation is conducted on machine instructions, where the context information is collected by simulating the execution of instructions. We further design a shared word mapping function using one single vocabulary for generating embeddings for both translations and queries. We evaluate the effectiveness of our technique, called TranCS, on the CodeSearchNet corpus with 1,000 queries. Experimental results show that TranCS significantly outperforms state-of-the-art techniques by 49.31% to 66.50% in terms of MRR (mean reciprocal rank).