A closer look into transformer-based code intelligence through code transformation: Challenges and opportunities
Transformer-based models have demonstrated state-of-the-art performance in many
intelligent coding tasks such as code comment generation and code completion. Previous …
intelligent coding tasks such as code comment generation and code completion. Previous …
Exploring the effectiveness of llms in automated logging generation: An empirical study
Automated logging statement generation supports developers in documenting critical
software runtime behavior. Given the great success in natural language generation and …
software runtime behavior. Given the great success in natural language generation and …
Exploring the effectiveness of llms in automated logging statement generation: An empirical study
Automated logging statement generation supports developers in documenting critical
software runtime behavior. While substantial recent research has focused on retrieval-based …
software runtime behavior. While substantial recent research has focused on retrieval-based …
Understanding the Robustness of Transformer-Based Code Intelligence via Code Transformation: Challenges and Opportunities
Transformer-based models have demonstrated state-of-the-art performance in various
intelligent coding tasks such as code comment generation and code completion. Previous …
intelligent coding tasks such as code comment generation and code completion. Previous …
Limits of machine learning for automatic vulnerability detection
Recent results of machine learning for automatic vulnerability detection have been very
promising indeed: Given only the source code of a function $ f $, models trained by machine …
promising indeed: Given only the source code of a function $ f $, models trained by machine …
An unbiased transformer source code learning with semantic vulnerability graph
Over the years, open-source software systems have become prey to threat actors. Even
highly-adopted software has been crippled by unforeseeable attacks, leaving millions of …
highly-adopted software has been crippled by unforeseeable attacks, leaving millions of …
Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs
With the recent unprecedented advancements in Artificial Intelligence (AI) computing,
progress in Large Language Models (LLMs) is accelerating rapidly, presenting challenges …
progress in Large Language Models (LLMs) is accelerating rapidly, presenting challenges …
Causative Insights into Open Source Software Security using Large Language Code Embeddings and Semantic Vulnerability Graph
Open Source Software (OSS) security and resilience are worldwide phenomena hampering
economic and technological innovation. OSS vulnerabilities can cause unauthorized …
economic and technological innovation. OSS vulnerabilities can cause unauthorized …
Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysis
Software security remains a critical concern, particularly as junior developers, often lacking
comprehensive knowledge of security practices, contribute to codebases. While there are …
comprehensive knowledge of security practices, contribute to codebases. While there are …
VulCausal: Robust Vulnerability Detection Using Neural Network Models from a Causal Perspective
H Kuang, J Zhang, F Yang, L Zhang, Z Huang… - … on Knowledge Science …, 2024 - Springer
Deep learning has showcased remarkable performance in source code vulnerability
detection. However, significant challenges persist in terms of generalization and handling …
detection. However, significant challenges persist in terms of generalization and handling …