activity
20192026
most citedOn the Relevance of Cross-project Learning with Nearest Neighbours for Commit Message Generation

4 citations · 4 across the 4 of their papers we have counts for

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Showing cs.SEShow all

9 papers · 1 filter

cs.SE2026

JETO-Bench: A Reproducible Benchmark for Execution Time Improvement Patches in Java

Khashayar Etemadi, Zhendong Su

Automated fixing of performance issues is gaining attention, but existing benchmarks of execution time improvement patches (ETIPs) target Python, C++, or .NET and are fixed dataset…

cs.SE2026

CppPerf: An Automated Pipeline and Dataset for Performance-Improving C++ Commits

Tommy Ho, Khashayar Etemadi, Zhendong Su

Recent progress in automated repair of performance bugs demands realistic, executable benchmarks. However, existing C++ performance benchmarks are largely built from competitive pr…

cs.SE2025

LLM-based Property-based Test Generation for Guardrailing Cyber-Physical Systems

Khashayar Etemadi, Marjan Sirjani, Mahshid Helali Moghadam +2

Cyber-physical systems (CPSs) are complex systems that integrate physical, computational, and communication subsystems. The heterogeneous nature of these systems makes their safety…

cs.SE2024

VeCoGen: Automating Generation of Formally Verified C Code with Large Language Models

Merlijn Sevenhuijsen, Khashayar Etemadi, Mattias Nyberg

Large language models have demonstrated impressive capabilities in generating code, yet they often produce programs with flaws or deviations from intended behavior, limiting their…

cs.SE2024

Mokav: Execution-driven Differential Testing with LLMs

Khashayar Etemadi, Bardia Mohammadi, Zhendong Su +1

It is essential to detect functional differences between programs in various software engineering tasks, such as automated program repair, mutation testing, and code refactoring. T…

cs.SE2024

CigaR: Cost-efficient Program Repair with LLMs

Dávid Hidvégi, Khashayar Etemadi, Sofia Bobadilla +1

Large language models (LLM) have proven to be effective at automated program repair (APR). However, using LLMs can be costly, with companies invoicing users by the number of tokens…