On the influence of model fragment properties on a machine learning-based approach for feature location

Ballarín, Manuel; Marcén, Ana Cristina; Pelechano, Vicente; Cetina, Carlos

doi:10.1016/j.infsof.2020.106430

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…The first is word vectorization, but the SIF model can only input vectorized words 22 . Descriptions, summaries, and source code files in software bug (8) www.nature.com/scientificreports/ reports are converted into vectors using Glove word embedding techniques 23 . The second is word probability calculation.…”

Section: Translation Software Maintenance Of Sentence Embedding Colla...mentioning

confidence: 99%

Design of intelligent module design for humanoid translation robot by combining the deep learning with blockchain technology

Yang

2023

Sci Rep

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To accelerate the deep application of deep learning in text data processing, an English statistical translation system is established and applied to the question answering of humanoid robot. Firstly, the model of machine translation based on recursive neural network is implemented. A crawler system is established to collect English movie subtitle data. On this basis, an English subtitle translation system is designed. Then, combined with sentence embedding technology, the Particle Swarm Optimization (PSO) algorithm of meta-heuristic algorithm is adopted to locate the defects of translation software. A translation robot automatic question and answer interactive module is constructed. Additionally, the hybrid recommendation mechanism based on personalized learning is built using blockchain technology. Finally, the performance of translation model and software defect location model is evaluated. The results show that the Recurrent Neural Network (RNN) embedding algorithm has certain effect of word clustering. RNN embedded model has a strong ability to process short sentences. The strongest translated sentences are between 11 and 39 words long, while the weakest translated sentences are between 71 and 79 words long. Therefore, the model must strengthen the processing of long sentences, especially character—level input. The average sentence length is much longer than word-level input. The model based on PSO algorithm shows good accuracy in different data sets. This model averages better performance on Tomcat, standard widget toolkits, and Java development tool datasets than other comparison methods. The average reciprocal rank and average accuracy of the weight combination of PSO algorithm are very high. Moreover, this method is greatly affected by the dimension of the word embedding model, and the 300-dimension word embedding model has the best effect. To sum up, this study proposes a good statistical translation model for humanoid robot English translation, which lays the foundation for intelligent interaction between humanoid robots.

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Section: Translation Software Maintenance Of Sentence Embedding Colla...mentioning

confidence: 99%

Design of intelligent module design for humanoid translation robot by combining the deep learning with blockchain technology

Yang

2023

Sci Rep

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“…Their results show that search-based software engineering (SBSE) techniques can be applied to locate features in product models. Recently machine learning techniques are widely used for feature location process [58,59,60]. The authors in [58] proposed a machine learning-based approach for feature location on models.…”

Section: Related Workmentioning

confidence: 99%

“…Recently machine learning techniques are widely used for feature location process [58,59,60]. The authors in [58] proposed a machine learning-based approach for feature location on models. The goal is to identify the model fragments that best realizes specific features.…”

Section: Related Workmentioning

confidence: 99%

A novel approach for Software Architecture Product Line Engineering

Kerdoudi

Ziadi

Tibermacine

et al. 2022

Journal of Systems and Software

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“…Learning-based Feature Location (IST '20) (Ballarín et al 2021): In prior work, we have proposed using five measurements to report the location problems. Through this paper, we explore the influence of three of the measurements (density, multiplicity, and dispersion) on a machine learning-based approach for feature location.…”

Section: On the Influence Of Model Fragment Properties In Machinementioning

confidence: 99%

Definition of Descriptive and Diagnostic Measurements for Model Fragment Retrieval

Naya¹

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On the influence of model fragment properties on a machine learning-based approach for feature location

Cited by 6 publications

References 37 publications

Design of intelligent module design for humanoid translation robot by combining the deep learning with blockchain technology

Design of intelligent module design for humanoid translation robot by combining the deep learning with blockchain technology

A novel approach for Software Architecture Product Line Engineering

Definition of Descriptive and Diagnostic Measurements for Model Fragment Retrieval

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