# Laminar: A New Serverless Stream-based Framework with Semantic Code Search and Code Completion

arXiv: https://arxiv.org/abs/2309.00584 · HTML: https://mlsanity.com/paper/2309.00584/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 1 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [ReACC: A Retrieval-Augmented Code Completion Framework](https://mlsanity.com/paper/2203.07722/) (2022)
- [CoSQA: 20,000+ Web Queries for Code Search and Question Answering](https://mlsanity.com/paper/2105.13239/) (2021)
- [SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation](https://mlsanity.com/paper/2108.04556/) (2021)
- [Occupy the Cloud: Distributed Computing for the 99%](https://mlsanity.com/paper/1702.04024/) (2017)
- [Serverless Computing: A Survey of Opportunities, Challenges and Applications](https://mlsanity.com/paper/1911.01296/) (2019)
- [A Comprehensive Review of State-of-The-Art Methods for Java Code Generation from Natural Language Text](https://mlsanity.com/paper/2306.06371/) (2023)

## Built on by (most cited first, 2 of 2)

- [Large Language Models to the Rescue: Reducing the Complexity in Scientific Workflow Development Using ChatGPT](https://mlsanity.com/paper/2311.01825/) (2023)
- [High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data](https://mlsanity.com/paper/2601.09334/) (2026)

## Abstract (arXiv)

This paper introduces Laminar, a novel serverless framework based on dispel4py, a parallel stream-based dataflow library. Laminar efficiently manages streaming workflows and components through a dedicated registry, offering a seamless serverless experience. Leveraging large lenguage models, Laminar enhances the framework with semantic code search, code summarization, and code completion. This contribution enhances serverless computing by simplifying the execution of streaming computations, managing data streams more efficiently, and offering a valuable tool for both researchers and practitioners.