Classification of product backlog itams in agile software development: a deep learning-based approach
By: Banujan, Kuhaneswaran
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Contributor(s): Ravi Kumar, Nirubikaa.
Publisher: Hyderabad IUP Publications 2022Edition: Vol,18(3), Sep.Description: 7-26p.Subject(s): EXTC Engineering
In:
IUP journal of information technologySummary: Agile software development (ASD) is an iterative mehod project management and software development that enables teams provide customar value more quickly with fewer complications. product backlog items (PBI) are prioritized list to be -implemented ASD requirements prior to beginning sprinting in the ASD, one of the most crucial duties the classification of PBI using machine learning and deep learning methods, the, authors sought to categorize the PBI into spikes,foundational stories,and user storises.they initially gathered data from numerous software development initiatives and web sources. each pbi as classified manually as spikes, foundational stories and preprocessed to remove superfluous text content.using the TF, IDF and GLO Ve techniques, the preprocessed PBI were then embedded with words.ANN and LSTM were used to classife the PBIs. the paper models combinations of TF, IDF+ANN, and GLO VE+LSTM. with an accuracy of 92.4%, the GLO Ve+LSTM model surpassed other deployed models.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0122 |
Agile software development (ASD) is an iterative mehod project management and software development that enables teams provide customar value more quickly with fewer complications. product backlog items (PBI) are prioritized list to be -implemented ASD requirements prior to beginning sprinting in the ASD, one of the most crucial duties the classification of PBI using machine learning and deep learning methods, the, authors sought to categorize the PBI into spikes,foundational stories,and user storises.they initially gathered data from numerous software development initiatives and web sources. each pbi as classified manually as spikes, foundational stories and preprocessed to remove superfluous text content.using the TF, IDF and GLO Ve techniques, the preprocessed PBI were then embedded with words.ANN and LSTM were used to classife the PBIs. the paper models combinations of TF, IDF+ANN, and GLO VE+LSTM. with an accuracy of 92.4%, the GLO Ve+LSTM model surpassed other deployed models.
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