Search Results for author:
Found 12 papers, 7 papers with code
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.
Ranked #2 on
Bug fixing
on SWE-bench
Exploiting Code Symmetries for Learning Program Semantics
This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture.
Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)
It is beneficial to develop an efficient machine-learning based method for addition using embedded hexadecimal digits.
NeuDep: Neural Binary Memory Dependence Analysis
Notably, NeuDep also outperforms the current state-of-the-art on these tasks.
Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity
We thus train the model to learn execution semantics from the functions' micro-traces, without any manual labeling effort.
XDA: Accurate, Robust Disassembly with Transfer Learning
We present XDA, a transfer-learning-based disassembly framework that learns different contextual dependencies present in machine code and transfers this knowledge for accurate and robust disassembly.
Efficient Formal Safety Analysis of Neural Networks
Our approach can check different safety properties and find concrete counterexamples for networks that are 10$\times$ larger than the ones supported by existing analysis techniques.
NEUZZ: Efficient Fuzzing with Neural Program Smoothing
However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs.
Formal Security Analysis of Neural Networks using Symbolic Intervals
In this paper, we present a new direction for formally checking security properties of DNNs without using SMT solvers.
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems
Finally, we show that retraining using the safety violations detected by VeriVis can reduce the average number of violations up to 60. 2%.
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
Most existing testing techniques for DNN-driven vehicles are heavily dependent on the manual collection of test data under different driving conditions which become prohibitively expensive as the number of test conditions increases.
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.
