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Found 5 papers, 1 papers with code
CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models
We evaluate the effectiveness of our approach by examining code language models in generating high-risk security weaknesses.
SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in Fine-tuned Source Code Models
In this paper, we contribute the first systematic approach that simulates various OOD scenarios along different dimensions of source code data properties and study the fine-tuned model behaviors in such scenarios.
SampleFix: Learning to Correct Programs by Efficient Sampling of Diverse Fixes
Therefore, we propose a deep generative model to automatically correct programming errors by learning a distribution over potential fixes.
IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis
In this work, we investigate the problem of revealing the functionality of a black-box agent.
SampleFix: Learning to Generate Functionally Diverse Fixes
Therefore, we propose a generative model that learns a distribution over potential fixes.
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Program Repair
on DeepFix
