A novel methodology to classify test cases using natural language processing and imbalanced learning

Tahvili, Sahar; Hatvani, Leo; Ramentol, Enislay; Pimentel, Rita; Afzal, Wasif; Herrera, Francisco

doi:10.1016/j.engappai.2020.103878

Cited by 31 publications

(13 citation statements)

References 45 publications

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“…The former mainly represents the computer's understanding of the meaning of natural language text, while the latter realizes the expression of relevant definite intention based on the natural language text [ 18 ]. From the perspective of linguistics, NLP can be divided into phonetic, lexical, syntactic, semantic, and pragmatic analysis; from the angle of computer, NLP can be divided into four types: sequence tagging, relationship judgment, relationship generation, and classification [ 19 , 20 ]. Shekhar et al proposed a Hindi recognition technology by combining NLP with DL-based neural network model.…”

Section: Methodsmentioning

confidence: 99%

The Use of BP Neural Network Algorithm and Natural Language Processing in the Impact of Social Audit on Enterprise Innovation Ability

Wang

Wen

2022

Computational Intelligence and Neuroscience

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At present, there are still some problems in the document management of enterprise innovation projects, such as non-standard management, lagging update, chaotic content, insufficient information, and insufficient application. There is still a lack of effective methods to evaluate the financing ability of enterprises. To solve the above problems, high technology expertise (HNTE) is taken as the research objects. Firstly, the relationship between social audit and enterprise technological innovation is analyzed, and on this basis, combined with natural language processing (NLP), an extraction method of project document information is proposed. Secondly, the evaluation index system of enterprise financing ability is constructed based on Back Propagation Neural Network (BPNN), and the technology innovation audit system of HNTEs. Finally, combined with the actual content, the proposed document audit method is evaluated. The results show that: the average accuracy rate of the NLP-based innovation project document audit method is 91.36%, the average recall rate is 96.34%, and the average F statistical value is 95.34%. Among them, the recall rate and F statistical value are about 2.3% and 1.4% higher than manual processing, respectively. The recall rate and F value are obviously better than those of manual processing methods, and the processing time of single document based on NLP is only 87.5 s. The processing time is nearly 50 times lower than that of manual processing, which greatly improves the processing efficiency of document information. The corresponding test results of each index selected based on the evaluation of enterprise financing ability are all below 0.1, which meets the requirements of consistency. The evaluation results of BPNN model on enterprise financing ability are highly consistent with the target value, and the prediction error is controlled within 0.02, which can provide more accurate prediction results. This research obtains a more accurate prediction model of enterprise financing ability evaluation, which provides technical support for social auditing and the innovation and development of enterprise technology, and provides a feasible route for the development of BPNN in the financial field.

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Section: Methodsmentioning

confidence: 99%

The Use of BP Neural Network Algorithm and Natural Language Processing in the Impact of Social Audit on Enterprise Innovation Ability

Wang

Wen

2022

Computational Intelligence and Neuroscience

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“…The algorithm is built by gradient descent to extract a document vector for ""unidentified" paragraphs. [23] We may obtain the learned values to construct feature vectors after training certain neural networks. These vectors are meant to reflect the document's definitions.…”

Section: Classification Of Test Cases Using Nlpmentioning

confidence: 99%

“…Using Doc2Vec and a clustering algorithm, we can build a connection between test case grammatic relatedness and computationally efficient. [23]…”

Section: Classification Of Test Cases Using Nlpmentioning

confidence: 99%

The Impact of NLP on Software Testing

Konreddy¹

2021

JUSST

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The number of applications being built and deployed everyday are increasing by leaps and bounds. To ensure the best user/client experience, the application needs to be free of bugs and other service issues. This marks the importance of testing phase in application development and deployment phase. Basically, testing is dissected into couple of parts being Manual Testing and Automation Testing. Manual testing, which is usually, an individual tester is given software guidance to execute. The tester would post the findings as “passed” or “failed” as per the guidance. But this kind of testing is very costly and time taking process. To eliminate these short comings, automation testing was introduced but it had very little scope and applications are limited. Now, that Artificial Intelligence has been foraying into many domains and has been showing significant impact over those domains. The core principles of Natural Language Processing that can be used in Software Testing are discussed in this paper. It also provides a glimpse at how Natural Language Processing and Software Testing will evolve in the future. Here we focus mainly on test case prioritization, predicting manual test case failure and generation of test cases from requirements utilizing NLP. The research indicates that NLP will improve software testing outcomes, and NLP-based testing will usher in a coming age of software testers work in the not-too-distant times.

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“…Shahid et al [ 26 ] used time-series methods to predict the number of COVID-19 deaths and recovery cases in ten major countries. Tahvili et al [ 30 ] proposed a natural language processing and data conversion approach that uses supervised learning methods to process and evaluate unbalanced data. Zhang et al [ 40 ] applied text mining and natural language processing techniques to construction accident reports and used support vector machines (SVM), linear regression (LR), decision tree (DT), and other models plus an ensemble model to classify the causes of accidents.…”

Section: Introductionmentioning

confidence: 99%

Analysis of agricultural exports based on deep learning and text mining

Hsu

2022

J Supercomput

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Agricultural exports are an important source of economic profit for many countries. Accurate predictions of a country’s agricultural exports month on month are key to understanding a country’s domestic use and export figures and facilitate advance planning of export, import, and domestic use figures and the resulting necessary adjustments of production and marketing. This study proposes a novel method for predicting the rise and fall of agricultural exports, called agricultural exports time series-long short-term memory (AETS-LSTM). The method applies Jieba word segmentation and Word2Vec to train word vectors and uses TF-IDF and word cloud to learn news-related keywords and finally obtain keyword vectors. This research explores whether the purchasing managers’ index (PMI) of each industry can effectively use the AETS-LSTM model to predict the rise and fall of agricultural exports. Research results show that the inclusion of keyword vectors in the PMI values of the finance and insurance industries has a relative impact on the prediction of the rise and fall of agricultural exports, which can improve the prediction accuracy for the rise and fall of agricultural exports by 82.61%. The proposed method achieves improved prediction ability for the chemical/biological/medical, transportation equipment, wholesale, finance and insurance, food and textiles, basic materials, education/professional, science/technical, information/communications/broadcasting, transportation and storage, retail, and electrical and machinery equipment categories, while its performance for the electrical and optical categories shows improved prediction by combining keyword vectors, and its accuracy for the accommodation and food service, and construction and real estate industries remained unchanged. Therefore, the proposed method offers improved prediction capacity for agricultural exports month on month, allowing agribusiness operators and policy makers to evaluate and adjust domestic and foreign production and sales.

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A novel methodology to classify test cases using natural language processing and imbalanced learning

Cited by 31 publications

References 45 publications

The Use of BP Neural Network Algorithm and Natural Language Processing in the Impact of Social Audit on Enterprise Innovation Ability

The Use of BP Neural Network Algorithm and Natural Language Processing in the Impact of Social Audit on Enterprise Innovation Ability

The Impact of NLP on Software Testing

Analysis of agricultural exports based on deep learning and text mining

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