84 citations · 190 across the 34 of their papers we have counts for
6 papers · 1 filter
On the Impact of Language Selection for Training and Evaluating Programming Language Models
Jonathan Katzy, Maliheh Izadi, Arie van Deursen
The recent advancements in Transformer-based Language Models have demonstrated significant potential in enhancing the multilingual capabilities of these models. The remarkable prog…
Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study
Tim van Dam, Maliheh Izadi, Arie van Deursen
Transformer-based pre-trained models have recently achieved great results in solving many software engineering tasks including automatic code completion which is a staple in a deve…
STACC: Code Comment Classification using SentenceTransformers
Ali Al-Kaswan, Maliheh Izadi, Arie van Deursen
Code comments are a key resource for information about software artefacts. Depending on the use case, only some types of comments are useful. Thus, automatic approaches to classify…
The (ab)use of Open Source Code to Train Large Language Models
Ali Al-Kaswan, Maliheh Izadi
In recent years, Large Language Models (LLMs) have gained significant popularity due to their ability to generate human-like text and their potential applications in various fields…
Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge
Ali Al-Kaswan, Maliheh Izadi, Arie van Deursen
Previous work has shown that Large Language Models are susceptible to so-called data extraction attacks. This allows an attacker to extract a sample that was contained in the train…
Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries
Ali Al-Kaswan, Toufique Ahmed, Maliheh Izadi +3
Reverse engineering binaries is required to understand and analyse programs for which the source code is unavailable. Decompilers can transform the largely unreadable binaries into…