1 citations · 2 across the 7 of their papers we have counts for
7 papers
Bogus Bugs, Duplicates, and Revealing Comments: Data Quality Issues in NPR
Julian Aron Prenner, Romain Robbes
The performance of a machine learning system is not only determined by the model but also, to a substantial degree, by the data it is trained on. With the increasing use of machine…
Simple Fault Localization using Execution Traces
Julian Aron Prenner, Romain Robbes
Traditional spectrum-based fault localization (SBFL) exploits differences in a program's coverage spectrum when run on passing and failing test cases. However, such runs can provid…
ThrowBench: Benchmarking LLMs by Predicting Runtime Exceptions
Julian Aron Prenner, Romain Robbes
Modern Large Language Models (LLMs) have shown astounding capabilities of code understanding and synthesis. In order to assess such capabilities, several benchmarks have been devis…
Extracting Fix Ingredients using Language Models
Julian Aron Prenner, Romain Robbes
Deep learning and language models are increasingly dominating automated program repair research. While previous generate-and-validate approaches were able to find and use fix ingre…
What the Fix? A Study of ASATs Rule Documentation
Corentin Latappy, Thomas Degueule, Jean-Rémy Falleri +3
Automatic Static Analysis Tools (ASATs) are widely used by software developers to diffuse and enforce coding practices. Yet, we know little about the documentation of ASATs, despit…
INSPECT: Intrinsic and Systematic Probing Evaluation for Code Transformers
Anjan Karmakar, Romain Robbes
Pre-trained models of source code have recently been successfully applied to a wide variety of Software Engineering tasks; they have also seen some practical adoption in practice,…
Out of Context: How important is Local Context in Neural Program Repair?
Julian Aron Prenner, Romain Robbes
Deep learning source code models have been applied very successfully to the problem of automated program repair. One of the standing issues is the small input window of current mod…