Deep learning (DL) techniques are rapidly developed and have been widely adopted in practice. However, similar to traditional software, DL systems also contain bugs, which could cause serious impacts especially in safety-critical domains. Recently, much research has focused on testing DL models, while little attention has been paid for testing DL libraries, which is the basis of building DL models and directly affects the behavior of DL systems. In this work, we propose a novel approach, LEMON, to testing DL libraries. In particular, we (1) design a series of mutation rules for DL models, with the purpose of exploring different invoking sequences of library code and hard-to-trigger behaviors; and (2) propose a heuristic strategy to guide the model generation process towards the direction of amplifying the inconsistent degrees of the inconsistencies between different DL libraries caused by bugs, so as to mitigate the impact of potential noise introduced by uncertain factors in DL libraries. We conducted an empirical study to evaluate the effectiveness of LEMON with 20 release versions of 4 widely-used DL libraries, i.e., TensorFlow, Theano, CNTK, MXNet. The results demonstrate that LEMON detected 24 new bugs in the latest release versions of these libraries, where 7 bugs have been confirmed and one bug has been fixed by developers. Besides, the results confirm that the heuristic strategy for model generation indeed effectively guides LEMON in amplifying the inconsistent degrees for bugs.

Deep neural network (DNN) has become increasingly popular and DNN testing is very critical to guarantee the correctness of DNN, i.e., the accuracy of DNN in this work. However, DNN testing suffers from a serious efficiency problem, i.e., it is costly to label each test input to know the DNN accuracy for the testing set, since labeling each test input involves multiple persons (even with domain-specific knowledge) in a manual way and the testing set is large-scale. To relieve this problem, we propose a novel and practical approach, called PACE (which is short for P ractical AC curacy E stimation), which selects a small set of test inputs that can precisely estimate the accuracy of the whole testing set. In this way, the labeling costs can be largely reduced by just labeling this small set of selected test inputs. Besides achieving a precise accuracy estimation, to make PACE more practical it is also required that it is interpretable, deterministic, and as efficient as possible. Therefore, PACE first incorporates clustering to interpretably divide test inputs with different testing capabilities (i.e., testing different functionalities of a DNN model) into different groups. Then, PACE utilizes the MMD-critic algorithm, a state-of-the-art example-based explanation algorithm, to select prototypes (i.e., the most representative test inputs) from each group, according to the group sizes, which can reduce the impact of noise due to clustering. Meanwhile, PACE also borrows the idea of adaptive random testing to select test inputs from the minority space (i.e., the test inputs that are not clustered into any group) to achieve great diversity under the required number of test inputs. The two parallel selection processes (i.e., selection from both groups and the minority space) compose the final small set of selected test inputs. We conducted an extensive study to evaluate the performance of PACE based on a comprehensive benchmark (i.e., 24 pairs of DNN models and testing sets) by considering different types of models (i.e., classification and regression models, high-accuracy and low-accuracy models, and CNN and RNN models) and different types of test inputs (i.e., original, mutated, and automatically generated test inputs). The results demonstrate that PACE is able to precisely estimate the accuracy of the whole testing set with only 1.181%∼2.302% deviations, on average, significantly outperforming the state-of-the-art approaches.

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The purpose of this research is to solve stress calculation problem of the composite pipe of multihole steel pipe and polyethylene of raised temperature (PE-RT). The formulas, used to calculate the burst pressure of the composite pipe, were established separately based on three kinds of different strength theories, and also the formula, used to calculate axial stress was developed based on the deviator strain energy theory. The calculation results were compared with the measured values. It shows that the calculation results of formula based on the deviator strain energy theory are very close to the measured values. This conclusion has important meaning to the heating directly buried installation of the composite pipe.

3D modeling, virtual assembly and motion simulation of an up-open fan gate mine skip were completed with Pro/Engineer software. During virtual design, faults of a mine skip such as static and dynamic interference can be found out prior to be made, which is helpful to optimize it. Furthermore, it is effective to shorten design period of mine skip and save development cost and improve design quality.

Many test coverage metrics have been proposed to measure the deep neural network (DNN) testing effectiveness, including structural coverage and nonstructural coverage. These test coverage metrics are proposed based on the fundamental assumption: They are correlated with test effectiveness. However, the fundamental assumption is still not validated sufficiently and reasonably, which brings question on the usefulness of DNN test coverage. This paper conducted a revisiting study on the existing DNN test coverage from the test effectiveness perspective, to effectively validate the fundamental assumption. Here, we carefully considered the diversity of subjects, three test effectiveness criteria, and both typical and state‐of‐the‐art test coverage metrics. Different from all the existing studies that deliver negative conclusions on the usefulness of existing DNN test coverage, we identified some positive conclusions on their usefulness from the test effectiveness perspective. In particular, we found the complementary relationship between structural and nonstructural coverage and identified the practical usage scenarios and promising research directions for these existing test coverage metrics.

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Nowadays there are many methods used in classification of tunnel rock masses, most of them are based on a large number of engineering examples, and they divide rock masses into different stability levels. As set up with the summary of numerous and dispersed data, they have universal usability. However, at the same time they lost pertinence for the specific case, and they can not accurately reflect the characteristics of different rock masses in different examples. So hierarchical cluster analysis is exploited to classify tunnel rock masses only for one example or examples in one region according to the dominant factor which affects the engineering properties of rock section most, and the interrelationship of different categories has been studied. Through the application in specific example, a pertinent hierarchical system has been established, which makes the rock classification to be a dynamic process. Keywords-classification of tunnel rock masses; hierarchical cluster analysis; dominant factor; pertinence; dynamic process I.