25 citations · 39 across the 4 of their papers we have counts for
7 papers · 1 filter
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…
Mining Software Repositories with a Collaborative Heuristic Repository
Hlib Babii, Julian Aron Prenner, Laurin Stricker +3
Many software engineering studies or tasks rely on categorizing software engineering artifacts. In practice, this is done either by defining simple but often imprecise heuristics,…
Empirical Standards for Software Engineering Research
Paul Ralph, Nauman bin Ali, Sebastian Baltes +39
Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer…