Zaynab Zahra
ABSTRACT
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.
- 2023. A comprehensive review of State-of-The-Art methods for Java code generation from Natural Language Text. Natural Language Processing Journal 3 (2023), 100013. https://doi.org/10.1016/j.nlp.2023.100013Google Scholar
Cross Ref
- Dirk Eddelbuettel. 2022. A Brief Introduction to Redis. arxiv:2203.06559 [stat.CO]Google Scholar
- Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. arxiv:2002.08155 [cs.CL]Google Scholar
- Rosa Filgueira, Amrey Krause, Malcolm Atkinson, Iraklis Klampanos, and Alexander Moreno. 2016. dispel4py: A Python Framework for Data-Intensive Scientific Computing. International Journal of High Performance Computing Applications (IJHPCA) (2016).Google Scholar
- Rosa Filgueira, Amrey Krause, Alessandro Spinuso, Iraklis Klampanos, Peter Danecek, and Malcolm Atkinson. 2015. Dispel4py: An Open-Source Python library for Data-Intensive Seismology. EGUGA (2015), 6790.Google Scholar
- Message P Forum. 1994. MPI: A Message-Passing Interface Standard. Technical Report. USA.Google ScholarDigital Library
- Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018. Deep Code Search. In Proceedings of the 40th International Conference on Software Engineering (Gothenburg, Sweden) (ICSE ’18). Association for Computing Machinery, New York, NY, USA, 933–944. https://doi.org/10.1145/3180155.3180167Google ScholarDigital Library
- Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022. UniXcoder: Unified Cross-Modal Pre-training for Code Representation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds.). Association for Computational Linguistics, 7212–7225. https://doi.org/10.18653/v1/2022.acl-long.499Google ScholarCross Ref
- Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2020. GraphCodeBERT: Pre-training Code Representations with Data Flow. https://doi.org/10.48550/ARXIV.2009.08366Google ScholarCross Ref
- Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021. GraphCodeBERT: Pre-training Code Representations with Data Flow. arxiv:2009.08366 [cs.SE]Google Scholar
- Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, and Nan Duan. 2021. CoSQA: 20, 000+ Web Queries for Code Search and Question Answering. CoRR abs/2105.13239 (2021). arXiv:2105.13239https://arxiv.org/abs/2105.13239Google Scholar
- Eric Jonas, Shivaram Venkataraman, Ion Stoica, and Benjamin Recht. 2017. Occupy the Cloud: Distributed Computing for the 99%. CoRR abs/1702.04024 (2017). arXiv:1702.04024http://arxiv.org/abs/1702.04024Google ScholarDigital Library
- P Carbone Asterios Katsifodimos, S Ewen Volker Markl, and S Haridi Kostas Tzoumas. 2015. Apache FlinkTM: Stream and batch processing in a single engine. Bull. IEEE Comput. Soc. Tech. Comm. Data Eng 36, 4 (2015).Google Scholar
- Manoj Kumar. 2019. Serverless architectures review, future trend and the solutions to open problems. American Journal of Software Engineering 6, 1 (2019), 1–10.Google ScholarCross Ref
- Zhuozhao Li, Ryan Chard, Yadu Babuji, Ben Galewsky, Tyler J. Skluzacek, Kirill Nagaitsev, Anna Woodard, Ben Blaiszik, Josh Bryan, Daniel S. Katz, Ian Foster, and Kyle Chard. 2022. Federated Function as a Service for Science. IEEE Transactions on Parallel and Distributed Systems 33, 12 (dec 2022), 4948–4963. https://doi.org/10.1109/tpds.2022.3208767Google ScholarCross Ref
- Zihao lI and Rosa Filgueira. 2023. Mapping the repository landscape: harnessing similarity with RepoSim and RepoSnipy. In 2023 IEEE 19th International Conference on e-Science (e-Science). IEEE. https://www.escience-conference.org/2023/ 19th IEEE International Conference on eScience, eScience ; Conference date: 09-10-2023 Through 13-10-2023.Google Scholar
- Liang Liang, Rosa Filgueira, Yan Yan, and Thomas Heinis. 2022. Scalable adaptive optimizations for stream-based workflows in multi-HPC-clusters and cloud infrastructures. Future Generation Computer Systems 128 (2022), 102–116. https://doi.org/10.1016/j.future.2021.09.036Google ScholarDigital Library
- Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung won Hwang, and Alexey Svyatkovskiy. 2022. ReACC: A Retrieval-Augmented Code Completion Framework. arxiv:2203.07722 [cs.SE]Google Scholar
- Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu. 2021. CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. CoRR abs/2102.04664 (2021).Google Scholar
- Ruchir Puri, David S. Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss. 2021. CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks. arxiv:2105.12655 [cs.SE]Google Scholar
- Johann Schleier-Smith, Vikram Sreekanti, Anurag Khandelwal, Joao Carreira, Neeraja J. Yadwadkar, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica, and David A. Patterson. 2021. What Serverless Computing is and Should Become: The next Phase of Cloud Computing. Commun. ACM 64, 5 (apr 2021), 76–84. https://doi.org/10.1145/3406011Google ScholarDigital Library
- Hossein Shafiei, Ahmad Khonsari, and Payam Mousavi. 2022. Serverless computing: a survey of opportunities, challenges, and applications. Comput. Surveys 54, 11s (2022), 1–32.Google ScholarDigital Library
- Shuai Wang, Jinyang Liu, Ye Qiu, Zhiyi Ma, Junfei Liu, and Zhonghai Wu. 2019. Deep learning based code completion models for programming codes. In Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control. 1–9.Google ScholarDigital Library
- Xin Wang, Yasheng Wang, Fei Mi, Pingyi Zhou, Yao Wan, Xiao Liu, Li Li, Hao Wu, Jin Liu, and Xin Jiang. 2021. SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation. https://doi.org/10.48550/ARXIV.2108.04556Google ScholarCross Ref
- Yue Wang, Weishi Wang, Shafiq Joty, and Steven C. H. Hoi. 2021. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. https://doi.org/10.48550/ARXIV.2109.00859Google ScholarCross Ref
- Christopher Williams and Rosa Filgueira. 2023. RepoGraph: a novel semantic code exploration tool for python repositories based on knowledge graphs and deep learning. In 2023 IEEE 19th International Conference on e-Science (e-Science). IEEE. https://www.escience-conference.org/2023/ 19th IEEE International Conference on eScience, eScience ; Conference date: 09-10-2023 Through 13-10-2023.Google Scholar
- Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019. HuggingFace’s Transformers: State-of-the-art Natural Language Processing. CoRR abs/1910.03771 (2019). arXiv:1910.03771Google Scholar
- Chunyan Zhang, Junchao Wang, Qinglei Zhou, Ting Xu, Ke Tang, Hairen Gui, and Fudong Liu. 2022. A Survey of Automatic Source Code Summarization. Symmetry 14, 3 (2022). https://www.mdpi.com/2073-8994/14/3/471Google Scholar
Index Terms
- Laminar: A New Serverless Stream-based Framework with Semantic Code Search and Code Completion
Index terms have been assigned to the content through auto-classification.
Recommendations
An Extractive-and-Abstractive Framework for Source Code Summarization
(Source) Code summarization aims to automatically generate summaries/comments for given code snippets in the form of natural language. Such summaries play a key role in helping developers understand and maintain source code. Existing code summarization ...
Supporting Multi-Provider Serverless Computing on the Edge
ICPP Workshops '18: Workshop Proceedings of the 47th International Conference on Parallel ProcessingServerless computing has recently emerged as a new execution model for cloud computing, in which service providers offer compute runtimes, also known as Function-as-a-Service (FaaS) platforms, allowing users to develop, execute and manage application ...
A Neural-Network based Code Summarization Approach by Using Source Code and its Call Dependencies
Internetware '19: Proceedings of the 11th Asia-Pacific Symposium on InternetwareCode summarization aims at generating natural language abstraction for source code, and it can be of great help for program comprehension and software maintenance. The current code summarization approaches have made progress with neural-network. However,...
Comments