Neural network based black box testing

Bhasin, Harsh; Khanna, Esha

doi:10.1145/2579281.2579292

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Research has shown that it has the same nonlinear approximation ability as BPNN. Still, it is especially suitable for small and medium sample data modeling that does not obey the ordinary distribution law [20][21][22][41][42][43][44][45][46]. Due to PPR, the model of independent variable weight sum is equal to 1 for multiple independent variables with collaborative constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Yu,

Liu,

Lai

2024

Sustainability

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The drastic fluctuations in pork prices directly affect the sustainable development of pig farming, agriculture, and feed processing industries, reducing people’s happiness and sense of gain. Although there have been extensive studies on pork price prediction and early warning in the literature, some problems still need further study. Based on the monthly time series data of pork prices and other 11 influencing prices (variables) such as beef, hog, piglet, etc., in China from January 2000 to November 2023, we have established a project pursuit auto-regression (PPAR) and a hybrid PPAR (H-PPAR) model. The results of the PPAR model study show that the monthly pork prices in the lagged periods one to three have an important impact on the current monthly pork price. The first lagged period has the largest and most positive impact. The second lagged period has the second and a negative impact. We built the H-PPAR model using the 11 independent variables (prices), including the prices of corn, hog, mutton, hen’s egg, and beef in lagged period one, the piglet’s price in lagged period six, and by deleting non-important variables. The results of the H-PPAR model show that the hog price in lagged period one is the most critical factor, and beef price and the other six influencing variables are essential factors. The model’s performance metrics show that the PPAR and H-PPAR models outperform approaches such as support vector regression, error backpropagation neural network, dynamic model average, etc., and possess better suitability, applicability, and reliability. Our results forecast the changing trend of the monthly pork price and provide policy insights for administrators and pig farmers to control and adjust the monthly pork price and further enhance the health and sustainable development of the hog farming industry.

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Section: Literature Reviewmentioning

confidence: 99%

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Yu,

Liu,

Lai

2024

Sustainability

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“…Along with the above mentioned things, it is important to reveal maximum number of bugs. In order to achieve this, test data for black box suite have been prioritized using NN approach [7]. The work used module interaction diagram (MID) generator [5] in order to determine the flow in which, modules call each other.…”

Section: Introductionmentioning

confidence: 99%

“…It therefore becomes essential to prioritize the test cases on the basis of their importance. The policies, that would determine the importance of test cases, have already been dealt with in one of the earlier works [7]. The model of automated test case generation has also been presented in one of our earlier works [5].…”

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confidence: 99%

Neural Network-Based Automated Priority Assigner

Bhasin

Khanna²,

Sharma

2015

Advances in Intelligent Systems and Computing

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The testing of a system starts with the crafting of test cases. Not all the test cases are, however, equally important. The test cases can be prioritized using policies discussed in the work. The work proposes a neural network model to prioritize the test cases. The work has been validated using backpropagation neural network. 200 test cases were crafted and the experiment was carried out using 2, 5, 10, 15, and 20 layers neural network. The results have been reported and lead to the conclusion that neural network-based priority analyzer can predict the priority of a test.

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Applicability of Soft Computing and Optimization Algorithms in Software Testing and Metrics – A Brief Review

Sharma

Chandra

2017

Advances in Intelligent Systems and Computing

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Neural network based black box testing

Cited by 6 publications

References 12 publications

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Neural Network-Based Automated Priority Assigner

Applicability of Soft Computing and Optimization Algorithms in Software Testing and Metrics – A Brief Review

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