Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review

• URL: http://arxiv.org/abs/2307.02503v1
• Date: Tue, 4 Jul 2023 21:26:51 GMT
• Title: Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review
• Authors: Man Fai Wong, Shangxin Guo, Ching Nam Hang, Siu Wai Ho, Chee Wei Tan
• Abstract summary: This paper focuses on transformer-based large language models (LLMs) trained using Big Code. LLMs have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection. It explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications.
• Score: 9.355153561673855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software naturalness, have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection. Notable examples of such applications include the GitHub Copilot powered by OpenAI's Codex and DeepMind AlphaCode. This paper presents an overview of the major LLMs and their applications in downstream tasks related to AI-assisted programming. Furthermore, it explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications, with a discussion on extending AI-assisted programming capabilities to Apple's Xcode for mobile software development. This paper also presents the challenges of and opportunities for incorporating NLP techniques with software naturalness, empowering developers with advanced coding assistance and streamlining the software development process.

Related papers

• Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing [0.9668407688201359]
  generative artificial intelligence (GenAI) is poised to transform productivity in scientific computing. We developed a tool, CodeScribe, which combines prompt engineering with user supervision to establish an efficient process for code conversion. We also address the challenges of AI-driven code translation and highlight its benefits for enhancing productivity in scientific computing.
  arXiv  Detail & Related papers  (2024-10-31T16:48:41Z)
• Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework [8.28588489551341]
  This paper presents CAMP, a multi-model AI-assisted programming framework that consists of a local model that employs Retrieval-Augmented Generation (RAG) RAG retrieves contextual information from the cloud model to facilitate context-aware prompt construction. The methodology is actualized in Copilot for Xcode, an AI-assisted programming tool crafted for the Apple software ecosystem.
  arXiv  Detail & Related papers  (2024-10-20T04:51:24Z)
• NoviCode: Generating Programs from Natural Language Utterances by Novices [59.71218039095155]
  We present NoviCode, a novel NL Programming task which takes as input an API and a natural language description by a novice non-programmer. We show that NoviCode is indeed a challenging task in the code synthesis domain, and that generating complex code from non-technical instructions goes beyond the current Text-to-Code paradigm.
  arXiv  Detail & Related papers  (2024-07-15T11:26:03Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• Natural Language-Oriented Programming (NLOP): Towards Democratizing Software Creation [4.5318695190841884]
  Natural Language-Oriented Programming (NLOP) is a vision introduced in this paper. It allows developers to articulate software requirements and logic in their natural language, thereby democratizing software creation. This paper reviews various programming models, assesses their contributions and limitations, and highlights that natural language will be the new programming language.
  arXiv  Detail & Related papers  (2024-06-08T09:13:54Z)
• CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
  We propose CodeGRAG, a Graphical Retrieval Augmented Code Generation framework to enhance the performance of LLMs. CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to fill the gap between programming languages and natural language. Various experiments and ablations are done on four datasets including both the C++ and python languages to validate the hard meta-graph prompt, the soft prompting technique, and the effectiveness of the objectives for pretrained GNN expert.
  arXiv  Detail & Related papers  (2024-05-03T02:48:55Z)
• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [56.019447113206006]
  Large Language Models (LLMs) have achieved remarkable progress in code generation. CodeIP is a novel multi-bit watermarking technique that embeds additional information to preserve provenance details. Experiments conducted on a real-world dataset across five programming languages demonstrate the effectiveness of CodeIP.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes [11.520721038793285]
  This study focuses on the initial experiences of 62 participants who used a contextualized coding AI assistant -- named StackSpot AI -- in a controlled setting. Assistants' use resulted in significant time savings, easier access to documentation, and the generation of accurate codes for internal APIs. challenges associated with the knowledge sources necessary to make the coding assistant access more contextual information as well as variable responses and limitations in handling complex codes were observed.
  arXiv  Detail & Related papers  (2023-11-30T10:52:28Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)
• Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets [0.0]
  The research applies AI-driven code assistants to analyze a selection of influential computer code that has shaped modern technology. The original contribution of this study was to examine half of the most significant code advances in the last 50 years.
  arXiv  Detail & Related papers  (2023-01-05T23:17:17Z)
• Competition-Level Code Generation with AlphaCode [74.87216298566942]
  We introduce AlphaCode, a system for code generation that can create novel solutions to problems that require deeper reasoning. In simulated evaluations on recent programming competitions on the Codeforces platform, AlphaCode achieved on average a ranking of top 54.3%.
  arXiv  Detail & Related papers  (2022-02-08T23:16:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.