5 citations · 11 across the 7 of their papers we have counts for
7 papers
SWE-Bench+: Enhanced Coding Benchmark for LLMs
Reem Aleithan, Haoran Xue, Mohammad Mahdi Mohajer +3
Large Language Models (LLMs) in Software Engineering (SE) can offer assistance for coding. To facilitate a rigorous evaluation of LLMs in practical coding contexts, Carlos et al. i…
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…
Program Slicing in the Era of Large Language Models
Kimya Khakzad Shahandashti, Mohammad Mahdi Mohajer, Alvine Boaye Belle +2
Program slicing is a critical technique in software engineering, enabling developers to isolate relevant portions of code for tasks such as bug detection, code comprehension, and d…
Evaluating the Effectiveness of GPT-4 Turbo in Creating Defeaters for Assurance Cases
Kimya Khakzad Shahandashti, Mithila Sivakumar, Mohammad Mahdi Mohajer +3
Assurance cases (ACs) are structured arguments that support the verification of the correct implementation of systems' non-functional requirements, such as safety and security, the…
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…
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…