143 citations · 291 across the 8 of their papers we have counts for
10 papers
RunBugRun -- An Executable Dataset for Automated Program Repair
Julian Aron Prenner, Romain Robbes
Recently, we can notice a transition to data-driven techniques in Automated Program Repair (APR), in particular towards deep neural networks. This entails training on hundreds of t…
JEMMA: An Extensible Java Dataset for ML4Code Applications
Anjan Karmakar, Miltiadis Allamanis, Romain Robbes
Machine Learning for Source Code (ML4Code) is an active research field in which extensive experimentation is needed to discover how to best use source code's richly structured info…
Codex Hacks HackerRank: Memorization Issues and a Framework for Code Synthesis Evaluation
Anjan Karmakar, Julian Aron Prenner, Marco D'Ambros +1
The Codex model has demonstrated extraordinary competence in synthesizing code from natural language problem descriptions. However, in order to reveal unknown failure modes and hid…
Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs
Julian Aron Prenner, Romain Robbes
OpenAI's Codex, a GPT-3 like model trained on a large code corpus, has made headlines in and outside of academia. Given a short user-provided description, it is capable of synthesi…
What do pre-trained code models know about code?
Anjan Karmakar, Romain Robbes
Pre-trained models of code built on the transformer architecture have performed well on software engineering (SE) tasks such as predictive code generation, code summarization, amon…
Making the most of small Software Engineering datasets with modern machine learning
Julian Aron Prenner, Romain Robbes
This paper provides a starting point for Software Engineering (SE) researchers and practitioners faced with the problem of training machine learning models on small datasets. Due t…