1 citations · 1 across the 5 of their papers we have counts for
9 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…