Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets
- URL: http://arxiv.org/abs/2301.03373v1
- Date: Thu, 5 Jan 2023 23:17:17 GMT
- Title: Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets
- Authors: David Noever, Kevin Williams
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The research applies AI-driven code assistants to analyze a selection of
influential computer code that has shaped modern technology, including email,
internet browsing, robotics, and malicious software. The original contribution
of this study was to examine half of the most significant code advances in the
last 50 years and, in some cases, to provide notable improvements in clarity or
performance. The AI-driven code assistant could provide insights into
obfuscated code or software lacking explanatory commentary in all cases
examined. We generated additional sample problems based on bug corrections and
code optimizations requiring much deeper reasoning than a traditional Google
search might provide. Future work focuses on adding automated documentation and
code commentary and translating select large code bases into more modern
versions with multiple new application programming interfaces (APIs) and
chained multi-tasks. The AI-driven code assistant offers a valuable tool for
software engineering, particularly in its ability to provide human-level
expertise and assist in refactoring legacy code or simplifying the explanation
or functionality of high-value repositories.
Related papers
- No Man is an Island: Towards Fully Automatic Programming by Code Search, Code Generation and Program Repair [9.562123938545522]
toolname can integrate various code search, generation, and repair tools, combining these three research areas together for the first time.
We conduct preliminary experiments to demonstrate the potential of our framework, eg helping CodeLlama solve 267 programming problems with an improvement of 62.53%.
arXiv Detail & Related papers (2024-09-05T06:24:29Z) - Code Compass: A Study on the Challenges of Navigating Unfamiliar Codebases [2.808331566391181]
We propose a novel tool, Code, to address these issues.
Our study highlights a significant gap in current tools and methodologies.
Our formative study demonstrates how effectively the tool reduces the time developers spend navigating documentation.
arXiv Detail & Related papers (2024-05-10T06:58:31Z) - How Far Have We Gone in Binary Code Understanding Using Large Language Models [51.527805834378974]
We propose a benchmark to evaluate the effectiveness of Large Language Models (LLMs) in binary code understanding.
Our evaluations reveal that existing LLMs can understand binary code to a certain extent, thereby improving the efficiency of binary code analysis.
arXiv Detail & Related papers (2024-04-15T14:44:08Z) - Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit [63.82016263181941]
Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora.
Currently, there is already a thriving research community focusing on code intelligence.
arXiv Detail & Related papers (2023-12-30T17:48:37Z) - 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) - Natural Language Generation and Understanding of Big Code for
AI-Assisted Programming: A Review [9.355153561673855]
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.
arXiv Detail & Related papers (2023-07-04T21:26:51Z) - Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code.
We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
arXiv Detail & Related papers (2023-02-14T18:43:34Z) - Enhancing Semantic Code Search with Multimodal Contrastive Learning and
Soft Data Augmentation [50.14232079160476]
We propose a new approach with multimodal contrastive learning and soft data augmentation for code search.
We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages.
arXiv Detail & Related papers (2022-04-07T08:49:27Z) - ReACC: A Retrieval-Augmented Code Completion Framework [53.49707123661763]
We propose a retrieval-augmented code completion framework, leveraging both lexical copying and referring to code with similar semantics by retrieval.
We evaluate our approach in the code completion task in Python and Java programming languages, achieving a state-of-the-art performance on CodeXGLUE benchmark.
arXiv Detail & Related papers (2022-03-15T08:25:08Z) - Project CodeNet: A Large-Scale AI for Code Dataset for Learning a
Diversity of Coding Tasks [11.10732802304274]
Project CodeNet consists of 14M code samples and about 500M lines of code in 55 different programming languages.
Project CodeNet is not only unique in its scale, but also in the diversity of coding tasks it can help benchmark.
arXiv Detail & Related papers (2021-05-25T00:13:29Z) - COSEA: Convolutional Code Search with Layer-wise Attention [90.35777733464354]
We propose a new deep learning architecture, COSEA, which leverages convolutional neural networks with layer-wise attention to capture the code's intrinsic structural logic.
COSEA can achieve significant improvements over state-of-the-art methods on code search tasks.
arXiv Detail & Related papers (2020-10-19T13:53:38Z)
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.