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## Abstract 背景： 1. the de facto standard to assess the quality of DNNs in the industry is to check their performance (accuracy) on a collected set of 阅读全文
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## Abstract ## 1. Intro ## 2. Background ### 2.1 Program Understanding and Generation Tasks ### 2.2 NL-PL Pre-Trained Models 这类的函数崩溃 本文：GRIST Github: https://github.com/Jacob-yen/GRIST Task: generate a small 阅读全文
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## Abstract 本文： Task: formalize the space of adversaries against DNNs and then introduce an adversarial testing 实验： 方法：defining a hardness measure 效果： 阅读全文
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## Abstract 背景： the robustness requires the model to produce consistent decisions given minorly perturbed code inputs 本文:CARROT Github: https://github 阅读全文
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## Abstract 阅读全文
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## Abstract 本文: DeepPERF Github: https://dlperf.github.io/ 对象：TensorFlow, Keras Task: 1. characterize symptoms, root causes, and introducing and expos 阅读全文
Proj CDeepFuzz Paper Reading: A Brief Introduction to Automatic Differentiation for Machine Learning
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## Abstract 本文：描述AD, its motivations, implementation approaches, dataflow programming, example programs with Tensorflow and PyTorch ## 1. Intro ## 2. 阅读全文
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## Abstract 背景： 1. AD涉及computational fluid dynamics, atmospheric sciences, engineering design optimization 2. AD与DL长时间并不相互交流，直到dynamic computational g 阅读全文
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## Abstract 背景：在architecture level detecting bugs获利更高 本文：DEBAR Github: https://github.com/ForeverZyh/DEBAR Task: static analysis of neural architectur 阅读全文