Latent Dirichlet Allocation (LDA) Based on Automated Bug Severity Prediction Model

Bibyan, Ritu; Anand, Sameer; Jaiswal, Ajay

doi:10.1007/978-981-16-6289-8_31

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Later in 2019, they extended their studies by focusing on multi‐attribute severity prediction along with the fix‐time prediction. An automated bug severity prediction model was proposed by extracting features through the TF‐IDF method using a neural network and extended the study by using LDA for feature extraction (Bibyan et al, 2020; Bibyan et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

Bug severity prediction using LDA and sentiment scores: A CNN approach

et al. 2023

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The crucial part of the software development cycle is software maintenance. The demands included in the software management are fault fixes and request to change or bring a new feature. If priority is not given to these demands, then it may lead to customer dissatisfaction, inefficient planning, and software failure as well. Therefore, it is important to study the severity of the bug reports to maintain the efficiency of the software. Various research has been conducted in the past to predict the severity of the paper using text mining focusing only on the content of the bug reports. The sentiment of the user while reporting a bug also plays a vital role. In this study, we will be focusing on two aspects, that is, sentiment and content to improve the prediction. We propose a prediction model based on LDA to study the content aspect and emotion analysis to study the sentiment aspect. The model is validated on the datasets collected from the Eclipse project using Convolutional Neural Network (CNN). The results show that the CNN model effectively utilizes the content and sentiment aspect of the data to handle the severity prediction. CNN has weight sharing feature that decreases the number of parameters used for training. It also improves generalization and overfitting is avoided The Accuracy, Precision, Recall, and F‐measure are improved when both aspects are taken into account rather than considering only content.

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Section: Related Workmentioning

confidence: 99%

Bug severity prediction using LDA and sentiment scores: A CNN approach

et al. 2023

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Software bug priority prediction technique based on intuitionistic fuzzy representation and class imbalance learning

Panda,

Nagwani

2023

Knowl Inf Syst

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Automatic Bug Classification System to Improve the Software Organization Product Performance

Kelin

Nagarajan

Rajendran

et al. 2022

International Journal of Sociotechnology and Knowledge Development

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Consistently, many bugs are raised, which are not completely settled, and countless designers are utilizing open sources or outsider assets, which prompts security issues. Bug-triage is the impending mechanized bug report framework to appoint individual security teams for a more than adequate pace of bug reports submitted from various IDEs inside the association (on-premises). We can lessen the time and cost of bug following and allocate it to the fitting group by foreseeing which division it has a place in within an association. In this paper, the authors are executing an automatic bug tracking system (ABTS) to allocate the group for the revealed bug involving the text examination for bug naming and characterization AI calculation for anticipating designer. Hybrid natural language processing and machine learning techniques are used for automatic bug identification to improve the performance of software organization products.

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Latent Dirichlet Allocation (LDA) Based on Automated Bug Severity Prediction Model

Cited by 3 publications

References 25 publications

Bug severity prediction using LDA and sentiment scores: A CNN approach

Bug severity prediction using LDA and sentiment scores: A CNN approach

Software bug priority prediction technique based on intuitionistic fuzzy representation and class imbalance learning

Automatic Bug Classification System to Improve the Software Organization Product Performance

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