Fuzzing Deep-Learning Libraries via Automated Relational API Inference

A growing body of research has been dedicated to DL model testing. However, there is still limited work on testing DL libraries, which serve as the foundations for building, training, and running DL models. Prior work on fuzzing DL libraries can only generate tests for APIs which have been invoked by documentation examples, developer tests, or DL models, leaving a large number of APIs untested. In this paper, we propose DeepREL, the first approach to automatically inferring relational APIs for more effective DL library fuzzing. Our basic hypothesis is that for a DL library under test, there may exist a number of APIs sharing similar input parameters and outputs; in this way, we can easily "borrow" test inputs from invoked APIs to test other relational APIs. Furthermore, we formalize the notion of value equivalence and status equivalence for relational APIs to serve as the oracle for effective bug finding. We have implemented DeepREL as a fully automated end-to-end relational API inference and fuzzing technique for DL libraries, which 1) automatically infers potential API relations based on API syntactic or semantic information, 2) synthesizes concrete test programs for invoking relational APIs, 3) validates the inferred relational APIs via representative test inputs, and finally 4) performs fuzzing on the verified relational APIs to find potential inconsistencies. Our evaluation on two of the most popular DL libraries, PyTorch and TensorFlow, demonstrates that DeepREL can cover 157% more APIs than state-of-the-art FreeFuzz. To date, DeepREL has detected 162 bugs in total, with 106 already confirmed by the developers as previously unknown bugs. Surprisingly, DeepREL has detected 13.5% of the high-priority bugs for the entire PyTorch issue-tracking system in a three-month period. Also, besides the 162 code bugs, we have also detected 14 documentation bugs (all confirmed).Comment: Accepted at ESEC/FSE 202

Vulnerability Analysis of Soft Caving Tunnel Support System and Surrounding Rock Optimal Control Technology Research

The vulnerability assessment model, composed by 11 vulnerability factors, is established with the introduction of the concept of “vulnerability” into the assessment of tunnel support system. Analytic hierarchy process is utilized to divide these 11 factors into human attributes and natural attributes, and define the weight of these factors for the model. The “vulnerability” applied io the assessment of the tunnel support system model is reached. The vulnerability assessment model was used for evaluating and modifying the haulage tunnel #3207 of Bo-fang mine panel #2. The results decreased the vulnerability of the tunnel support system and demonstrated acceptable effects. Furthermore, the results show that the impact of human attributes on tunnel support systems is dramatic under the condition that natural attributes are permanent, and the “vulnerability” is exactly a notable factor to manifest the transformation during this process. The results also indicate that optimizing human attributes can attenuate vulnerability in tunnel support systems. As a result, enhancement of stability of tunnel support systems can be achieved

Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty

Low-rank tensor completion (LRTC) is an important problem in computer vision and machine learning. The minimax-concave penalty (MCP) function as a non-convex relaxation has achieved good results in the LRTC problem. To makes all the constant parameters of the MCP function as variables so that futherly improving the adaptability to the change of singular values in the LRTC problem, we propose the bivariate equivalent minimax-concave penalty (BEMCP) theorem. Applying the BEMCP theorem to tensor singular values leads to the bivariate equivalent weighted tensor $\Gamma$-norm (BEWTGN) theorem, and we analyze and discuss its corresponding properties. Besides, to facilitate the solution of the LRTC problem, we give the proximal operators of the BEMCP theorem and BEWTGN. Meanwhile, we propose a BEMCP model for the LRTC problem, which is optimally solved based on alternating direction multiplier (ADMM). Finally, the proposed method is applied to the data restorations of multispectral image (MSI), magnetic resonance imaging (MRI) and color video (CV) in real-world, and the experimental results demonstrate that it outperforms the state-of-arts methods.Comment: arXiv admin note: text overlap with arXiv:2109.1225

White-box Compiler Fuzzing Empowered by Large Language Models

Compiler correctness is crucial, as miscompilation falsifying the program behaviors can lead to serious consequences. In the literature, fuzzing has been extensively studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates tests without sufficient understanding of internal compiler behaviors. As such, they often fail to construct programs to exercise conditions of intricate optimizations. Meanwhile, traditional white-box techniques are computationally inapplicable to the giant codebase of compilers. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation/understanding tasks and have achieved state-of-the-art performance in black-box fuzzing. Nonetheless, prompting LLMs with compiler source-code information remains a missing piece of research in compiler testing. To this end, we propose WhiteFox, the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization. WhiteFox adopts a dual-model framework: (i) an analysis LLM examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; (ii) a generation LLM produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are used as feedback to further enhance the test generation on the fly. Our evaluation on four popular compilers shows that WhiteFox can generate high-quality tests to exercise deep optimizations requiring intricate conditions, practicing up to 80 more optimizations than state-of-the-art fuzzers. To date, WhiteFox has found in total 96 bugs, with 80 confirmed as previously unknown and 51 already fixed. Beyond compiler testing, WhiteFox can also be adapted for white-box fuzzing of other complex, real-world software systems in general

EBSD study of microstructure evolution of ferritic stainless steel during cold rolling and annealing

The microstructure and texture of ferritic stainless steels (FSSs), formed during cold rolling and annealing processes, determine the mechanical properties of final sheet, especially the deep drawing formability. In this work, aNb, Ti stabilized17%Cr FSS was cold rolled with the reductions of 20%~70% and annealed for periods at 700°C. EBSD technique was used to characterize the microstructure evolution and inhomogeneous deformation strain distribution of the sheet during cold rolling. Partially annealed sheets were also analyzed to observe the nucleation and growth of recrystallized grains. Special attentions were paid on the crystal orientation of the deformed grains and recrystallzed grains. The results infer that in-grain shear band was formed in the cold rolled sample with the reduction higher than 30%, associated with the formation of high deformation strains. And the recrystallized grains prefer to form at some unique grain boundaries and in-grain shear bands. The orientations of recrystallized grains relates to the deformed grains

EBSD study of microstructure evolution of ferritic stainless steel during cold rolling and annealing

The microstructure and texture of ferritic stainless steels (FSSs), formed during cold rolling and annealing processes, determine the mechanical properties of final sheet, especially the deep drawing formability. In this work, aNb, Ti stabilized17%Cr FSS was cold rolled with the reductions of 20%~70% and annealed for periods at 700°C. EBSD technique was used to characterize the microstructure evolution and inhomogeneous deformation strain distribution of the sheet during cold rolling. Partially annealed sheets were also analyzed to observe the nucleation and growth of recrystallized grains. Special attentions were paid on the crystal orientation of the deformed grains and recrystallzed grains. The results infer that in-grain shear band was formed in the cold rolled sample with the reduction higher than 30%, associated with the formation of high deformation strains. And the recrystallized grains prefer to form at some unique grain boundaries and in-grain shear bands. The orientations of recrystallized grains relates to the deformed grains

The suppressive effect of co-inhibiting PD-1 and CTLA-4 expression on H22 hepatomas in mice

Abstract Objective We investigated the suppressive effect of siRNA-mediated co-inhibition of PD-1 and CTLA-4 expression on H22 hepatomas in mice. Methods Murine H22 cells were cultured in vivo in ICR mice. An allograft tumor model was also established in another ICR mouse group. The tumor-bearing mice were randomly divided into four groups: control, single PD-1 siRNA, single CTLA-4 siRNA, and double PD-1 + CTLA-4 siRNAs. The survival time and physiological condition of the mice were observed after the injection of the siRNAs and placebo. The volume and weight of the solid tumor were measured to assess the inhibition of the tumor. To assess the effects of siRNAs on mouse immune function, the protein levels of IFN-γ and IL-10 in the blood and PD-L1 in the tumor and liver were determined using ELISA, and the mRNA levels of IFN-γ, PD-L1, PD-1, CTLA-4, IL-6 and Survivin in the tumor, liver and spleen were determined using quantitative RT-PCR. The ratios of Bax and Bcl-2 protein were determined via western blot to analyze the effect of siRNAs on tumor cell apoptosis. Results The anti-tumor effect appeared in all groups with siRNA-mediated inhibition. The tumor growth suppression was stronger in the group with double inhibition. The weight and volume of the tumors were significantly lower and the survival rate improved in the three siRNA groups. IFN-γ levels increased but IL-10 levels decreased in the blood of the siRNA group mice compared with the results for the control group. In the tumor and spleen tissue, the IFN-γ levels significantly increased, but in the liver tissue they significantly decreased in the three siRNA groups. The results of quantitative RT-PCR showed that the mRNAs for PD-1 and CTLA-4 were downregulated in spleen tissue in the three siRNA groups, while the PD-L1 mRNA and protein levels increased significantly in the tumor, but decreased in the liver. Survivin and IL-6 mRNA levels decreased in the tumor. Western blot results showed that ratio of Bax and Bcl-2 had significantly increased. These results indicated that downregulating PD-1 and CTLA-4 could increase the body’s immune response and promote apoptosis of tumor cells. Conclusion Co-inhibiting the expressions of PD-1 and CTLA-4 can effectively suppress the growth of H22 hepatoma and promote the apoptosis of tumor cells in mice. Blocking PD-1 and CTLA-4 can improve the vitality of T cells, and improve the immune environment and response