Decompiling binary executables to high-level code is an important step in reverse engineering scenarios, such as malware analysis and legacy code maintenance.
Vulnerability identification is crucial to protect software systems from attacks for cyber-security.
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 general accuracy of a model at the same time.
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.
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts.
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.
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.
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.
It is challenging to search for executables relevant to an obfuscated application for developers to analyze efficiently.
Software Engineering Cryptography and Security