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## Abstract 背景: 1. 对微小扰动十分脆弱主要来自于overfitting、limited datasets 2. 由于Specification往往不完备,因此automatic program repair通常要从程序行为中学习 本文:SENSEI, SENSEI-SA Githu 阅读全文
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## Abstract 本文:DLFuzz Task: Fuzz Machine Learning Models using differential testing to detect inconsistency before/after perturbation with minutely mu 阅读全文
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## Abstract 本文: DLFuzz 方法: Adversarial inputs + Fuzzing, Differential Testing between before/after adding perturb 实验: 数据集:MNIST LeNet, ImageNet + VGG/ 阅读全文
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## Abstract 本文:graph-based fuzz testing Task: Fuzzing Machine Learning Libraries using operator graph Github: https://github.com/gbftdlie/G (Q?https:/ 阅读全文
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## Abstract 本文: RapidFuzz Task: use GAN to fuzz program with highly-structured inputs Method: use GAN to capture the data structure, then locate the h 阅读全文
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## Abstract 背景:Python API使得DL Libraries的参数类型难以确定 本文:FreeFuzz Github: https://github.com/ise-uiuc/FreeFuzz 方法:从1. 来自库文档的代码片段 2. 测试 3. DL models in the 阅读全文
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## Abstract 本文:CocoFuzzing Task: test ML Models, test code processing models 方法:10 mutators to automatically generate validly and semantically preserv 阅读全文
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## Abstract 背景:已有的方法仅仅利用了已经存在的models,只在model inference阶段检测bugs 本文: Muffin 方法:specifically-designed model fuzzing approach + 定制一组指标来衡量不同DL库之间的不一致differ 阅读全文
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## Abstract 背景: 1. Q: 现有的方法没有考虑到small perturbations的影响,或者这些perturbation只能限定在某个特定的模型上使用,在其他模型上则本身就不符合example要求(可能是指标签就会改变?) 2. 现有的方法多使用浅层的feature const 阅读全文
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## Abstract 本文: DeepHunter Task: Fuzzing Deep Learning Models Github: https://github.com/Shimmer93/Deephunter-backup Method: 1. Metamorphic mutation t 阅读全文
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## Abstract 背景:现有的constraint-extraction tech 对extracting DL-specific input constraints效率太低 本文:DocTer Github: https://github.com/lin-tan/DocTer Task: e 阅读全文
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## Abstract 背景: 1. 目前已有工作test underlying operator-level DL Libraries,但测试compier的工作比较少 2. compiler: compile high-level tensor computation graphs into h 阅读全文
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## Abstract 背景:马尔可夫决策过程(Markov decision process, MDP)是串联决策问题(sequential decision making)的一种数学化建模;机器学习已经为MDP提供了很多解法,但这些解法没有被严格测试过,或者不真正可靠(Q?) 本文:MDPFuz 阅读全文
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## Abstract 背景:Deep-learning compiler例如TVM和TensorRT被使用来optimize DNN模型以达到更好的性能要求;在Deep Learning Compiler中的bug可能导致语义改变 本文:NNSmith Task: fuzz deep-learni 阅读全文
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## Abstract 本文:TitanFuzz Task: use LLM to fuzz DL librarires Method: use both generative and infilling LLMs(e.g., Codex, InCoder) 实验: 对象:Tensorflow, P 阅读全文
Proj CDeepFuzz Paper Reading: Fuzzing Deep-Learning Libraries via Automated Relational API Inference
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## Abstract 背景:使用API间sharing similar input parameters and inputs的关系能更有效测试DL Libraries 本文: DeepREL Github: https://github.com/ise-uiuc/DeepREL Task: in 阅读全文
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## Abstract 背景: 1. In property-based testing(PBT), one asserts properties that a function should satisfy and the system automatically generates tests 阅读全文
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## Abstract 背景: 挑战: 1. need valid input domain of each API function 2. hard to trigger new behavior 本文:SkipFuzz 任务: fuzz machine learning libraries us 阅读全文
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## Abstract 本文:ConFL Task: Fuzz Deep Learning Libraries using Constraint-guided fuzzer Method: Constraint-guided fuzzer + grouping 实验: 对象:Tensorflow(M 阅读全文
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## Abstract 本文:DeFuzz Task: guided directed fuzzing Method: pre-trained BiLSTM to identify the potentially vulnerable functions, directed fuzzing 实验: 阅读全文