# Senatus -- A Fast and Accurate Code-to-Code Recommendation Engine

arXiv: https://arxiv.org/abs/2111.04473 · HTML: https://mlsanity.com/paper/2111.04473/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Unit Test Case Generation with Transformers and Focal Context](https://mlsanity.com/paper/2009.05617/) (2020)
- [DeepBugs: A Learning Approach to Name-based Bug Detection](https://mlsanity.com/paper/1805.11683/) (2018)
- [Language-Agnostic Representation Learning of Source Code from Structure and Context](https://mlsanity.com/paper/2103.11318/) (2021)
- [Code Completion with Neural Attention and Pointer Networks](https://mlsanity.com/paper/1711.09573/) (2017)
- [When Deep Learning Met Code Search](https://mlsanity.com/paper/1905.03813/) (2019)
- [SourcererCC: Scaling Code Clone Detection to Big Code](https://mlsanity.com/paper/1512.06448/) (2015)
- [Aroma: Code Recommendation via Structural Code Search](https://mlsanity.com/paper/1812.01158/) (2018)
- [LSH Ensemble: Internet-Scale Domain Search](https://mlsanity.com/paper/1603.07410/) (2016)
- [Opportunities and Challenges in Code Search Tools](https://mlsanity.com/paper/2011.02297/) (2020)
- [Oreo: Detection of Clones in the Twilight Zone](https://mlsanity.com/paper/1806.05837/) (2018)
- [Neural Code Search Evaluation Dataset](https://mlsanity.com/paper/1908.09804/) (2019)
- [Learning Python Code Suggestion with a Sparse Pointer Network](https://mlsanity.com/paper/1611.08307/) (2016)
- [CoNCRA: A Convolutional Neural Network Code Retrieval Approach](https://mlsanity.com/paper/2009.01959/) (2020)
- [Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science](https://mlsanity.com/paper/1803.09835/) (2018)

## Built on by (most cited first, 3 of 3)

- [Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study](https://mlsanity.com/paper/2305.03017/) (2023)
- [DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection](https://mlsanity.com/paper/2310.06703/) (2023)
- [Deduplicating and Ranking Solution Programs for Suggesting Reference Solutions](https://mlsanity.com/paper/2307.07940/) (2023)

## Abstract (arXiv)

Machine learning on source code (MLOnCode) is a popular research field that has been driven by the availability of large-scale code repositories and the development of powerful probabilistic and deep learning models for mining source code. Code-to-code recommendation is a task in MLOnCode that aims to recommend relevant, diverse and concise code snippets that usefully extend the code currently being written by a developer in their development environment (IDE). Code-to-code recommendation engines hold the promise of increasing developer productivity by reducing context switching from the IDE and increasing code-reuse. Existing code-to-code recommendation engines do not scale gracefully to large codebases, exhibiting a linear growth in query time as the code repository increases in size. In addition, existing code-to-code recommendation engines fail to account for the global statistics of code repositories in the ranking function, such as the distribution of code snippet lengths, leading to sub-optimal retrieval results. We address both of these weaknesses with Senatus, a new code-to-code recommendation engine. At the core of Senatus is De-Skew LSH a new locality sensitive hashing (LSH) algorithm that indexes the data for fast (sub-linear time) retrieval while also counteracting the skewness in the snippet length distribution using novel abstract syntax tree-based feature scoring and selection algorithms. We evaluate Senatus and find the recommendations to be of higher quality than competing baselines, while achieving faster search. For example on the CodeSearchNet dataset Senatus improves performance by 31.21% F1 and 147.9x faster query time compared to Facebook Aroma. Senatus also outperforms standard MinHash LSH by 29.2% F1 and 51.02x faster query time.