Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws

• URL: http://arxiv.org/abs/2504.16310v1
• Date: Tue, 22 Apr 2025 23:07:24 GMT
• Title: Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws
• Authors: Leonardo Centellas-Claros, Juan J. Alonso-Lecaros, Juan Pablo Sandoval Alcocer, Andres Neyem,
• Abstract summary: We propose the creation of a synthetic dataset consisting of vulnerability-focused reviews that specifically comment on security flaws.<n>Our approach leverages Large Language Models (LLMs) to generate human-like code review comments for vulnerabilities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automation of code reviews using AI models has garnered substantial attention in the software engineering community as a strategy to reduce the cost and effort associated with traditional peer review processes. These models are typically trained on extensive datasets of real-world code reviews that address diverse software development concerns, including testing, refactoring, bug fixes, performance optimization, and maintainability improvements. However, a notable limitation of these datasets is the under representation of code vulnerabilities, critical flaws that pose significant security risks, with security-focused reviews comprising a small fraction of the data. This scarcity of vulnerability-specific data restricts the effectiveness of AI models in identifying and commenting on security-critical code. To address this issue, we propose the creation of a synthetic dataset consisting of vulnerability-focused reviews that specifically comment on security flaws. Our approach leverages Large Language Models (LLMs) to generate human-like code review comments for vulnerabilities, using insights derived from code differences and commit messages. To evaluate the usefulness of the generated synthetic dataset, we plan to use it to fine-tune three existing code review models. We anticipate that the synthetic dataset will improve the performance of the original code review models.

Related papers

• Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning [59.25951947621526]
  We propose an approach which can transform existing coding benchmarks into scoring and ranking datasets to evaluate the effectiveness of synthetic verifiers.<n>We release four new benchmarks (HE-R, HE-R+, MBPP-R, and MBPP-R+), and analyzed synthetic verification methods with standard, reasoning-based, and reward-based LLMs.<n>Our experiments show that reasoning can significantly improve test case generation and that scaling the number of test cases enhances the verification accuracy.
  arXiv  Detail & Related papers  (2025-02-19T15:32:11Z)
• Harnessing Large Language Models for Curated Code Reviews [2.5944208050492183]
  In code review, generating structured and relevant comments is crucial for identifying code issues and facilitating accurate code changes.<n>Existing code review datasets are often noisy and unrefined, posing limitations to the learning potential of AI models.<n>We propose a curation pipeline designed to enhance the quality of the largest publicly available code review dataset.
  arXiv  Detail & Related papers  (2025-02-05T18:15:09Z)
• 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)
• SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models [54.78329741186446]
  We propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. Experiments across both in-domain and out-of-domain benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.
  arXiv  Detail & Related papers  (2024-08-28T06:33:03Z)
• Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval [20.959848710829878]
  Large language models (LLMs) have brought significant advancements to code generation and code repair. However, their training using unsanitized data from open-source repositories, like GitHub, raises the risk of inadvertently propagating security vulnerabilities. We aim to present a comprehensive study aimed at precisely evaluating and enhancing the security aspects of code LLMs.
  arXiv  Detail & Related papers  (2024-07-02T16:13:21Z)
• Vulnerability Detection with Code Language Models: How Far Are We? [40.455600722638906]
  PrimeVul is a new dataset for training and evaluating code LMs for vulnerability detection. It incorporates a novel set of data labeling techniques that achieve comparable label accuracy to human-verified benchmarks. It also implements a rigorous data de-duplication and chronological data splitting strategy to mitigate data leakage issues.
  arXiv  Detail & Related papers  (2024-03-27T14:34:29Z)
• Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation [24.668682498171776]
  Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like GitHub, introduces the risk of inadvertently propagating security vulnerabilities. This paper presents a comprehensive study focused on evaluating and enhancing code LLMs from a software security perspective.
  arXiv  Detail & Related papers  (2023-10-25T00:32:56Z)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
  Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities. This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
  arXiv  Detail & Related papers  (2023-02-08T11:54:07Z)
• ReCode: Robustness Evaluation of Code Generation Models [90.10436771217243]
  We propose ReCode, a comprehensive robustness evaluation benchmark for code generation models. We customize over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. With human annotators, we verified that over 90% of the perturbed prompts do not alter the semantic meaning of the original prompt.
  arXiv  Detail & Related papers  (2022-12-20T14:11:31Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)
• Multi-context Attention Fusion Neural Network for Software Vulnerability Identification [4.05739885420409]
  We propose a deep learning model that learns to detect some of the common categories of security vulnerabilities in source code efficiently. The model builds an accurate understanding of code semantics with a lot less learnable parameters. The proposed AI achieves 98.40% F1-score on specific CWEs from the benchmarked NIST SARD dataset.
  arXiv  Detail & Related papers  (2021-04-19T11:50:36Z)

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.