397 citations · 594 across the 8 of their papers we have counts for
9 papers
Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules
Steve Kommrusch, Martin Monperrus, Louis-Noël Pouchet
We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax…
Neural Transfer Learning for Repairing Security Vulnerabilities in C Code
Zimin Chen, Steve Kommrusch, Martin Monperrus
In this paper, we address the problem of automatic repair of software vulnerabilities with deep learning. The major problem with data-driven vulnerability repair is that the few ex…
A Software-Repair Robot based on Continual Learning
Benoit Baudry, Zimin Chen, Khashayar Etemadi +8
Software bugs are common and correcting them accounts for a significant part of costs in the software development and maintenance process. This calls for automatic techniques to de…
Coherence Traffic in Manycore Processors with Opaque Distributed Directories
Steve Kommrusch, Marcos Horro, Louis-Noël Pouchet +2
Manycore processors feature a high number of general-purpose cores designed to work in a multithreaded fashion. Recent manycore processors are kept coherent using scalable distribu…
Equivalence of Dataflow Graphs via Rewrite Rules Using a Graph-to-Sequence Neural Model
Steve Kommrusch, Théo Barollet, Louis-Noël Pouchet
In this work we target the problem of provably computing the equivalence between two programs represented as dataflow graphs. To this end, we formalize the problem of equivalence b…
Artificial Intelligence Techniques for Security Vulnerability Prevention
Steve Kommrusch
Computer security has been a concern for decades and artificial intelligence techniques have been applied to the area for nearly as long. Most of the techniques are being applied t…
Using Sequence-to-Sequence Learning for Repairing C Vulnerabilities
Zimin Chen, Steve Kommrusch, Martin Monperrus
Software vulnerabilities affect all businesses and research is being done to avoid, detect or repair them. In this article, we contribute a new technique for automatic vulnerabilit…
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
Zimin Chen, Steve Kommrusch, Michele Tufano +3
This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a system, called SequenceR, for fixing…
Synthetic Lung Nodule 3D Image Generation Using Autoencoders
Steve Kommrusch, Louis-Noël Pouchet
One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train. A representative example is automated lu…