most citedThe Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models

3 citations · 3 across the 5 of their papers we have counts for

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cs.SE2026

The Poisoned Chalice of LLM Evaluation Report

Jonathan Katzy, Ali Al-Kaswan, Razvan Mihai Popescu +1

Large language models are increasingly used to evaluate and support software engineering tasks, yet the validity of these evaluations is often undermined by uncertainty about wheth…

cs.SE2026

Evaluating Non-English Developer Support in Machine Learning for Software Engineering

Jonathan Katzy, Yongcheng Huang, Gopal-Raj Panchu +5

Large Language Models are increasingly used in software engineering, but both code generation and its evaluation remain predominantly English-centric. This leaves a major gap in ou…

cs.SE2025

A Qualitative Investigation into LLM-Generated Multilingual Code Comments and Automatic Evaluation Metrics

Jonathan Katzy, Yongcheng Huang, Gopal-Raj Panchu +5

Large Language Models are essential coding assistants, yet their training is predominantly English-centric. In this study, we evaluate the performance of code language models in no…

cs.SE2024

An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets

Jonathan Katzy, Răzvan-Mihai Popescu, Arie van Deursen +1

Does the training of large language models potentially infringe upon code licenses? Furthermore, are there any datasets available that can be safely used for training these models…

cs.SE2024

Language Models for Code Completion: A Practical Evaluation

Maliheh Izadi, Jonathan Katzy, Tim van Dam +3

Transformer-based language models for automatic code completion have shown great promise so far, yet the evaluation of these models rarely uses real data. This study provides both…