Automatic Generation of Test Cases Based on Genetic Algorithm and RBF Neural Network

Liu, Zhenpeng; Xian-wei, Yang; Zhang, Shichen; Liu, Yi; Zhao, Yongjiu; Zheng, Weihua

doi:10.1155/2022/1489063

Cited by 3 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to copyright restrictions, we cannot release the collected images but will provide the corresponding URLs as an alternative. Although several publicly available rendering‐style datasets exist, 31–34 their face resolution is insufficient for high‐quality digital apparel sample display, 31,34 or they only contain a small number of rendered faces, 32,33 or they are rendered using a small number of face models (100 different identities) 34 . DRFHQ is the first high‐quality rendering‐style dataset with a face region resolution of

102{4}^2

that can be extended to downstream tasks, to the best of our knowledge.…”

Section: Methodsmentioning

confidence: 99%

Identity‐consistent transfer learning of portraits for digital apparel sample display

Wang,

Wu,

Yang

et al. 2024

Computer Animation & Virtual

View full text Add to dashboard Cite

The rapid development of the online apparel shopping industry demands innovative solutions for high‐quality digital apparel sample displays with virtual avatars. However, developing such displays is prohibitively expensive and prone to the well‐known “uncanny valley” effect, where a nearly human‐looking artifact arouses eeriness and repulsiveness, thus affecting the user experience. To effectively mitigate the “uncanny valley” effect and improve the overall authenticity of digital apparel sample displays, we present a novel photo‐realistic portrait generation framework. Our key idea is to employ transfer learning to learn an identity‐consistent mapping from the latent space of rendered portraits to that of real portraits. During the inference stage, the input portrait of an avatar can be directly transferred to a realistic portrait by changing its appearance style while maintaining the facial identity. To this end, we collect a new dataset, Daz‐Rendered‐Faces‐HQ (DRFHQ), specifically designed for rendering‐style portraits. We leverage this dataset to fine‐tune the StyleGAN2‐FFHQ generator, using our carefully crafted framework, which helps to preserve the geometric and color features relevant to facial identity. We evaluate our framework using portraits with diverse gender, age, and race variations. Qualitative and quantitative evaluations, along with ablation studies, highlight our method's advantages over state‐of‐the‐art approaches.

show abstract

102{4}^2

that can be extended to downstream tasks, to the best of our knowledge.…”

Section: Methodsmentioning

confidence: 99%

Identity‐consistent transfer learning of portraits for digital apparel sample display

Wang,

Wu,

Yang

et al. 2024

Computer Animation & Virtual

View full text Add to dashboard Cite

show abstract

“…The model employs a two-layer CNN to extract significant features from the best filter. The CNN convolution layers extract data feature information on multiple levels [ 41 ] and process the feature during the pooling layers to obtain the best classification features. The collected feature information is then passed to the layers of IndRNN, which uses it to learn more about the feature dependencies.…”

Section: Cnn’s Model Setting and Phasesmentioning

confidence: 99%

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

Hamdy

Ismail

Awad

et al. 2023

Sensors

View full text Add to dashboard Cite

MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems’ prediction accuracy and reliability are frequently harmed because of the small data size, human factors’ limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model’s main advantage is the enhanced accuracy in plant miRNA–IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method.

show abstract

“…Liu et al, Mishra et al, and Shihao ey al. also replace the fitness function, training a model to predict which code will be covered by input [51][52][53]. These models would be used when there is no tool support to measure coverage, or in cases where measuring coverage would be expensive.…”

Section: System Test Generationmentioning

confidence: 99%

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

Fontes

Gay

2023

Software Testing Verif & Rel

View full text Add to dashboard Cite

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.

show abstract

Automatic Generation of Test Cases Based on Genetic Algorithm and RBF Neural Network

Cited by 3 publications

References 22 publications

Identity‐consistent transfer learning of portraits for digital apparel sample display

Identity‐consistent transfer learning of portraits for digital apparel sample display

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

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

Contact Info

Product

Resources

About