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20242026
most citedThe Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models

3 citations · 3 across the 7 of their papers we have counts for

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5 papers · 1 filter

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

Reliable and Developer-Aligned Evaluation of Agents for Software Engineering

Razvan Mihai Popescu

Large language models are rapidly moving towards closing the development cycle, transitioning from simple assistive companions to autonomous contributors deeply embedded into colla…

cs.SE2026

Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time

Razvan Mihai Popescu, David Gros, Andrei Botocan +3

The rise of large language models for code has reshaped software development. Autonomous coding agents, able to create branches, open pull requests, and perform code reviews, now a…

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…