9 citations · 17 across the 11 of their papers we have counts for
11 papers
SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection
Md Basim Uddin Ahmed, Nima Shiri Harzevili, Jiho Shin +2
Large Language Models (LLMs) have shown promise in software engineering tasks, but evaluating their effectiveness in vulnerability detection is challenging due to the lack of high-…
Checker Bug Detection and Repair in Deep Learning Libraries
Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Jiho Shin +8
Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and…
A Survey on Query-based API Recommendation
Moshi Wei, Nima Shiri Harzevili, Alvine Boaye Belle +6
Application Programming Interfaces (APIs) are designed to help developers build software more effectively. Recommending the right APIs for specific tasks has gained increasing atte…
Benchmarking Deep Learning Fuzzers
Nima Shiri Harzevili, Hung Viet Pham, Song Wang
In this work, we set out to conduct the first ground-truth empirical evaluation of state-of-the-art DL fuzzers. Specifically, we first manually created an extensive DL bug benchmar…
SkipAnalyzer: A Tool for Static Code Analysis with Large Language Models
Mohammad Mahdi Mohajer, Reem Aleithan, Nima Shiri Harzevili +4
We introduce SkipAnalyzer, a large language model (LLM)-powered tool for static code analysis. SkipAnalyzer has three components: 1) an LLM-based static bug detector that scans sou…
A First Look at Fairness of Machine Learning Based Code Reviewer Recommendation
Mohammad Mahdi Mohajer, Alvine Boaye Belle, Nima Shiri harzevili +5
The fairness of machine learning (ML) approaches is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML…
Automatic Static Bug Detection for Machine Learning Libraries: Are We There Yet?
Nima Shiri harzevili, Jiho Shin, Junjie Wang +2
Automatic detection of software bugs is a critical task in software security. Many static tools that can help detect bugs have been proposed. While these static bug detectors are m…
Automatic Unit Test Generation for Deep Learning Frameworks based on API Knowledge
Arunkaleeshwaran Narayanan, Nima Shiri harzevili, Junjie Wang +3
Many automatic unit test generation tools that can generate unit test cases with high coverage over a program have been proposed. However, most of these tools are ineffective on de…
A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning
Nima Shiri Harzevili, Alvine Boaye Belle, Junjie Wang +4
Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures t…
Security Knowledge-Guided Fuzzing of Deep Learning Libraries
Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Moshi Wei +2
Recently, many Deep Learning fuzzers have been proposed for testing of DL libraries. However, they either perform unguided input generation (e.g., not considering the relationship…
Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries
Nima Shiri Harzevili, Jiho Shin, Junjie Wang +1
The application of machine learning (ML) libraries has been tremendously increased in many domains, including autonomous driving systems, medical, and critical industries. Vulnerab…