Convolutional Neural Network Based Approval Prediction of Enhancement Reports

For a given software enhancement report, identifying its possible approval status could help software developers by suggesting feature enhancements to compete in the software industry. An automatic solution for the approval prediction of enhancements could assist all the participants in resolving en...
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Gespeichert in:

Autor*in:	Jun Cheng [verfasserIn] Mazhar Sadiq [verfasserIn] Olga A. Kalugina [verfasserIn] Sadeem Ahmad Nafees [verfasserIn] Qasim Umer [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Deep learning algorithms classification enhancement reports approval prediction

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 122412-122424
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:122412-122424

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3108624

Katalog-ID:	DOAJ062740113

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authorswithroles_txt_mv	Jun Cheng @@aut@@ Mazhar Sadiq @@aut@@ Olga A. Kalugina @@aut@@ Sadeem Ahmad Nafees @@aut@@ Qasim Umer @@aut@@
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callnumber-first	T - Technology
author	Jun Cheng
spellingShingle	Jun Cheng misc TK1-9971 misc Deep learning algorithms misc classification misc enhancement reports misc approval prediction misc Electrical engineering. Electronics. Nuclear engineering Convolutional Neural Network Based Approval Prediction of Enhancement Reports
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topic_title	TK1-9971 Convolutional Neural Network Based Approval Prediction of Enhancement Reports Deep learning algorithms classification enhancement reports approval prediction
topic	misc TK1-9971 misc Deep learning algorithms misc classification misc enhancement reports misc approval prediction misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Deep learning algorithms misc classification misc enhancement reports misc approval prediction misc Electrical engineering. Electronics. Nuclear engineering
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title	Convolutional Neural Network Based Approval Prediction of Enhancement Reports
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title_full	Convolutional Neural Network Based Approval Prediction of Enhancement Reports
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title_sort	convolutional neural network based approval prediction of enhancement reports
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title_auth	Convolutional Neural Network Based Approval Prediction of Enhancement Reports
abstract	For a given software enhancement report, identifying its possible approval status could help software developers by suggesting feature enhancements to compete in the software industry. An automatic solution for the approval prediction of enhancements could assist all the participants in resolving enhancements. The key challenges are the preprocessing of noisy textual information and the state-of-the-art feature models to combine the syntactical and semantic word information available in the given text. To this end, we propose a deep learning based approach for the approval prediction of enhancement reports that incorporates the users’ sentiments involved in the text. First, we preprocess the textual information of all enhancement reports to avoid noise. Second, we compute the sentiment of each enhancement report using Senti4SD. Third, we combine the bag-of-words (BOW) representation and traditional word2vec based representation to learn the novel deep representation (a recurrent neural network (RNN) with attention based representation) of preprocessed text. Using an attention mechanism enables the model to remember the context over a long sequence of words in an enhancement report. Fourth, based on sentiment and deep representation, we train a deep learning based classifier for the approval prediction of enhancement reports. Finally, we reuse the 40, 000 enhancement reports from 10 real software applications to evaluate the proposed approach. The cross-application evaluation suggests that the proposed approach is accurate and outperforms the state-of-the-art. The results of the proposed approach improve the precision from 86.52% to 90.56%, recall from 66.45% to 80.10%, and f-measure from 78.12% to 85.01%.
abstractGer	For a given software enhancement report, identifying its possible approval status could help software developers by suggesting feature enhancements to compete in the software industry. An automatic solution for the approval prediction of enhancements could assist all the participants in resolving enhancements. The key challenges are the preprocessing of noisy textual information and the state-of-the-art feature models to combine the syntactical and semantic word information available in the given text. To this end, we propose a deep learning based approach for the approval prediction of enhancement reports that incorporates the users’ sentiments involved in the text. First, we preprocess the textual information of all enhancement reports to avoid noise. Second, we compute the sentiment of each enhancement report using Senti4SD. Third, we combine the bag-of-words (BOW) representation and traditional word2vec based representation to learn the novel deep representation (a recurrent neural network (RNN) with attention based representation) of preprocessed text. Using an attention mechanism enables the model to remember the context over a long sequence of words in an enhancement report. Fourth, based on sentiment and deep representation, we train a deep learning based classifier for the approval prediction of enhancement reports. Finally, we reuse the 40, 000 enhancement reports from 10 real software applications to evaluate the proposed approach. The cross-application evaluation suggests that the proposed approach is accurate and outperforms the state-of-the-art. The results of the proposed approach improve the precision from 86.52% to 90.56%, recall from 66.45% to 80.10%, and f-measure from 78.12% to 85.01%.
abstract_unstemmed	For a given software enhancement report, identifying its possible approval status could help software developers by suggesting feature enhancements to compete in the software industry. An automatic solution for the approval prediction of enhancements could assist all the participants in resolving enhancements. The key challenges are the preprocessing of noisy textual information and the state-of-the-art feature models to combine the syntactical and semantic word information available in the given text. To this end, we propose a deep learning based approach for the approval prediction of enhancement reports that incorporates the users’ sentiments involved in the text. First, we preprocess the textual information of all enhancement reports to avoid noise. Second, we compute the sentiment of each enhancement report using Senti4SD. Third, we combine the bag-of-words (BOW) representation and traditional word2vec based representation to learn the novel deep representation (a recurrent neural network (RNN) with attention based representation) of preprocessed text. Using an attention mechanism enables the model to remember the context over a long sequence of words in an enhancement report. Fourth, based on sentiment and deep representation, we train a deep learning based classifier for the approval prediction of enhancement reports. Finally, we reuse the 40, 000 enhancement reports from 10 real software applications to evaluate the proposed approach. The cross-application evaluation suggests that the proposed approach is accurate and outperforms the state-of-the-art. The results of the proposed approach improve the precision from 86.52% to 90.56%, recall from 66.45% to 80.10%, and f-measure from 78.12% to 85.01%.
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title_short	Convolutional Neural Network Based Approval Prediction of Enhancement Reports
url	https://doi.org/10.1109/ACCESS.2021.3108624 https://doaj.org/article/0096db12e70e4e7bace63d2fcb7d4da2 https://ieeexplore.ieee.org/document/9524733/ https://doaj.org/toc/2169-3536
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author2	Mazhar Sadiq Olga A. Kalugina Sadeem Ahmad Nafees Qasim Umer
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up_date	2024-07-03T13:47:03.342Z
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Convolutional Neural Network Based Approval Prediction of Enhancement Reports

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