activity
20182026
most citedAn Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

56 citations · 65 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SE2026

Natural Language-Focused Software Engineering via Code-Documentation Equivalence

Aryaz Eghbali, Zhongxin Liu, Michael Pradel

Source code documentation is an integral part of software development and maintenance, as it helps in understanding the code and facilitates communication among developers. However…

cs.CR2025

PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages

Deniz Simsek, Aryaz Eghbali, Michael Pradel

Security vulnerabilities in software packages are a significant concern for developers and users alike. Patching these vulnerabilities in a timely manner is crucial to restoring th…

cs.SE2024★ 9 cited

De-Hallucinator: Mitigating LLM Hallucinations in Code Generation Tasks via Iterative Grounding

Aryaz Eghbali, Michael Pradel

Large language models (LLMs) trained on datasets of publicly available source code have established a new state of the art in code generation tasks. However, these models are mostl…

cs.SE2023★ 56 cited

An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Max Schäfer, Sarah Nadi, Aryaz Eghbali +1

Unit tests play a key role in ensuring the correctness of software. However, manually creating unit tests is a laborious task, motivating the need for automation. Large Language Mo…

cs.DC2018

An Almost Tight RMR Lower Bound for Abortable Test-And-Set

Aryaz Eghbali, Philipp Woelfel

We prove a lower bound of Omega(log n/loglog n) for the remote memory reference (RMR) complexity of abortable test-and-set (leader election) in the cache-coherent (CC) and the dist…