A Vulnerability Code Intent Summary Dataset
- URL: http://arxiv.org/abs/2504.08180v1
- Date: Fri, 11 Apr 2025 00:39:50 GMT
- Title: A Vulnerability Code Intent Summary Dataset
- Authors: Yifan Huang, Weisong Sun, Yubin Qu,
- Abstract summary: This paper proposes an innovative large-scale multi-perspective Code Intent Summary dataset named BADS.<n>It aims to increase the understanding of a given code snippet and reduce the risk in the code developing process.<n>Our dataset and related tools have been publicly released on GitHub.
- Score: 3.609135490386991
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In the era of Large Language Models (LLMs), the code summarization technique boosts a lot, along with the emergence of many new significant works. However, the potential of code summarization in the Computer Security Area still remains explored. Can we generate a code summary of a code snippet for its security intention? Thus, this work proposes an innovative large-scale multi-perspective Code Intent Summary Dataset named BADS , aiming to increase the understanding of a given code snippet and reduce the risk in the code developing process. The procedure of establishing a dataset can be divided into four steps: First, we collect samples of codes with known vulnerabilities as well as code generated by AI from multiple sources. Second, we do the data clean and format unification, then do the data combination. Third, we utilize the LLM to automatically Annotate the code snippet. Last, We do the human evaluation to double-check. The dataset contains X code examples which cover Y categories of vulnerability. Our data are from Z open-source projects and CVE entries, and compared to existing work, our dataset not only contains original code but also code function summary and security intent summary, providing context information for research in code security analysis. All information is in CSV format. The contributions of this paper are four-fold: the establishment of a high-quality, multi-perspective Code Intent Summary Dataset; an innovative method in data collection and processing; A new multi-perspective code analysis framework that promotes cross-disciplinary research in the fields of software engineering and cybersecurity; improving the practicality and scalability of the research outcomes by considering the code length limitations in real-world applications. Our dataset and related tools have been publicly released on GitHub.
Related papers
- SnipGen: A Mining Repository Framework for Evaluating LLMs for Code [51.07471575337676]
Language Models (LLMs) are trained on extensive datasets that include code repositories.
evaluating their effectiveness poses significant challenges due to the potential overlap between the datasets used for training and those employed for evaluation.
We introduce SnipGen, a comprehensive repository mining framework designed to leverage prompt engineering across various downstream tasks for code generation.
arXiv Detail & Related papers (2025-02-10T21:28:15Z) - OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [76.59316249991657]
Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems.<n>While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited.<n>We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
arXiv Detail & Related papers (2024-11-07T17:47:25Z) - Contextualized Data-Wrangling Code Generation in Computational Notebooks [131.26365849822932]
We propose an automated approach, CoCoMine, to mine data-wrangling code generation examples with clear multi-modal contextual dependency.
We construct CoCoNote, a dataset containing 58,221 examples for Contextualized Data-wrangling Code generation in Notebooks.
Experiment results demonstrate the significance of incorporating data context in data-wrangling code generation.
arXiv Detail & Related papers (2024-09-20T14:49:51Z) - HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data [60.75578581719921]
Large language models (LLMs) have shown great potential for automatic code generation.
Recent studies highlight that many LLM-generated code contains serious security vulnerabilities.
We introduce HexaCoder, a novel approach to enhance the ability of LLMs to generate secure codes.
arXiv Detail & Related papers (2024-09-10T12:01:43Z) - An Exploratory Study on Fine-Tuning Large Language Models for Secure Code Generation [17.69409515806874]
We present an exploratory study on whether fine-tuning pre-trained LLMs on datasets of vulnerability-fixing commits can promote secure code generation.
We crawled a fine-tuning dataset for secure code generation by collecting code fixes of confirmed vulnerabilities from open-source repositories.
Our exploration reveals that fine-tuning LLMs can improve secure code generation by 6.4% in C language and 5.4% in C++ language.
arXiv Detail & Related papers (2024-08-17T02:51:27Z) - VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development.
We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM)
We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
arXiv Detail & Related papers (2024-06-11T16:15:06Z) - CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data
and Language Models of Code [6.491009626125319]
We introduce CodeLL, a lifelong learning dataset focused on code changes.
Our dataset aims to comprehensively capture code changes across the entire release history of open-source software repositories.
CodeLL enables researchers studying the behaviour of LMs in lifelong fine-tuning settings for learning code changes.
arXiv Detail & Related papers (2023-12-20T01:20:24Z) - Discriminating Human-authored from ChatGPT-Generated Code Via
Discernable Feature Analysis [2.9398911304923447]
This paper specifically aims to distinguish code generated by ChatGPT from that authored by humans.
We devise a dataset cleansing technique, which employs temporal and spatial segmentation, to mitigate the dearth of datasets.
To further enrich data resources, we employ "code transformation," "feature transformation," and "feature customization" techniques, generating an extensive dataset comprising 10,000 lines of ChatGPT-generated code.
arXiv Detail & Related papers (2023-06-26T03:15:06Z) - The Vault: A Comprehensive Multilingual Dataset for Advancing Code
Understanding and Generation [5.2510537676167335]
We present The Vault, a dataset of high-quality code-text pairs in multiple programming languages.
Our evaluations show that when fine-tuning Code Large Language Models on The Vault, such models outperform the same models trained on other datasets such as CodeSearchNet.
arXiv Detail & Related papers (2023-05-09T09:35:03Z) - 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) - CoDesc: A Large Code-Description Parallel Dataset [4.828053113572208]
We present CoDesc -- a large parallel dataset composed of 4.2 million Java methods and natural language descriptions.
With extensive analysis, we identify and remove prevailing noise patterns from the dataset.
We show that the dataset helps improve code search by up to 22% and achieves the new state-of-the-art in code summarization.
arXiv Detail & Related papers (2021-05-29T05:40:08Z)
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.