A Hybridized Artificial Neural Network for Automated Software Test Oracle

Kamaraj, K.; Lanitha, B.; Karthic, S.; Prakash, P. N. Senthil; Mahaveerakannan, R.

doi:10.32604/csse.2023.029703

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Kanaga Suba Raja S et al (2023) examined the aims to investigate the methods used to extract bodies of water using satellite Remote Sensing (RS) [20]. Research into the prediction of agricultural yield using variables like soil, crop traits, and water has been an encouraging field of research.…”

Section: Literature Survey Usmanmentioning

confidence: 99%

Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System

Kumar

2024

JMC

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Agriculture is the most vital sector for the global food supply, and it also provides raw materials for other types of industries. A crop recommendation system is essential for farmers who want to get the most out of their crop-choosing decisions. Over the last several decades, the world's ability to produce food has grown substantially owing to the extensive usage of fertilizers. Therefore, there has to be a more eco-friendly and effective way to utilize fertilizers that include nitrogen (N), phosphorous (P), and potassium (K) to ensure food security. For the reason, this study proposes an ensemble machine learning–assisted crop and fertilizer recommendation system (EML–CFRS) to maximize agricultural output while ensuring the correct use of mineral resources. The research used a dataset obtained from the Kaggle repository like that people can assess several distinct ML algorithms. The databases include data on three climate variables—temperature, rainfall, and humidity—and information on NPK and soil pH. The yields agricultural crops were used to train these models, including Decision Tree, KNN, XGBoost, Support Vector Machine, and Random Forest. Depending on the current weather and soil conditions, the trained model may then recommend the optimal fertiliser for a certain crop. Predicting the ideal kind and quantity of fertilizer for different crops was accomplished with a 96.5% accuracy rate by our suggested strategy.

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Section: Literature Survey Usmanmentioning

confidence: 99%

Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System

Kumar

2024

JMC

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“…When ANN models predict water quality in practice, the network approximation and generalization ability is highly dependent on the training sample, and the selection and analysis of the training set data is a difficult problem to solve. So far, there is no mature theoretical method to determine the number of nodes in a hidden layer network, relying mainly on experience to determine the number of nodes [9][10].…”

Section: Problems In the Studymentioning

confidence: 99%

Raman Spectroscopy System for Water Pollution Control based on Artificial Neural Network

2023

WPPCP

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Water resources are an important material resource for living and production. China is a country with poor water resources, and at the same time, the existing water resources in China are all polluted to varying degrees, especially the quality of surface water is closely related to the quality of people's production and life. With the boom in artificial neural network (ANN) research, neural networks have now been used in a number of fields such as graphics processing, expert decision making systems, sound processing, etc. due to the advantages of ANNs themselves, which have achieved amazing results. The theory has turned into a new multifaceted avant-garde discipline associated with multiple fields. In recent years, ANN research has been gradually applied to environmental science, some of which have applied ANN research to areas such as water eutrophication prediction and water quality prediction. The application of ANN technology to surface water quality prediction is at an early stage, and its characteristics make it a great advantage in this field. This paper investigates the use of BP ANNs to predict surface water quality, to make rapid and accurate predictions of surface water pollution, and to provide decisions for the protection of water resources and pollution prevention.

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“…The model predicts the correctness of output or properties of the expected output. Test verdicts: The majority of studies employ neural networks to train models that directly predict whether a test should pass or fail [97][98][99][100][101][102][103]. Most are simple, traditional neural networks for simple programs.…”

Section: Test Oracle Generationmentioning

confidence: 99%

The integration of machine learning into automated test generation: A systematic mapping study

Fontes

Gay

2023

Software Testing Verif & Rel

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Machine learning (ML) may enable effective automated test generation. We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges in this intersection by performing. We perform a systematic mapping study on a sample of 124 publications. ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property‐based, and expected output oracles. Supervised learning—often based on neural networks—and reinforcement learning—often based on Q‐learning—are common, and some publications also employ unsupervised or semi‐supervised learning. (Semi‐/Un‐)Supervised approaches are evaluated using both traditional testing metrics and ML‐related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. The work‐to‐date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed—and how they are applied—benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.

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A Hybridized Artificial Neural Network for Automated Software Test Oracle

Cited by 7 publications

References 25 publications

Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System

Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System

Raman Spectroscopy System for Water Pollution Control based on Artificial Neural Network

The integration of machine learning into automated test generation: A systematic mapping study

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