Search Results for author:
Found 10 papers, 7 papers with code
DIRECT : A Transformer-based Model for Decompiled Identifier Renaming
Decompiling binary executables to high-level code is an important step in reverse engineering scenarios, such as malware analysis and legacy code maintenance.
CYCLE: Learning to Self-Refine the Code Generation
Pre-trained code language models have achieved promising performance in code generation and improved the programming efficiency of human developers.
Vignat: Vulnerability identification by learning code semantics via graph attention networks
Vulnerability identification is crucial to protect software systems from attacks for cyber-security.
Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain
In this paper, we first formally define the self-consistency of Code LLMs and then design a framework, IdentityChain, which effectively and efficiently evaluates the self-consistency and conventional accuracy of a model at the same time.
REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models
This paper introduces a novel code-to-code search technique that enhances the performance of Large Language Models (LLMs) by including both static and dynamic features as well as utilizing both similar and dissimilar examples during training.
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts.
Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles
Self-driving cars and trucks, autonomous vehicles (AVs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and reliability -- which can most practically and convincingly be achieved by testing.
Testing DNN Image Classifiers for Confusion & Bias Errors
We found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others.
A Case Study on the Impact of Similarity Measure on Information Retrieval based Software Engineering Tasks
In particular, we analyze two previously proposed tools for project recommendation and bug localization tasks, which leverage diverse software artifacts, and observe that an informed choice of similarity measure indeed leads to improved performance of the existing SE tools.
Obfuscation Resilient Search through Executable Classification
It is challenging to search for executables relevant to an obfuscated application for developers to analyze efficiently.
Software Engineering Cryptography and Security
