Bug severity prediction using <scp>LDA</scp> and sentiment scores: A <scp>CNN</scp> approach

Bibyan, Ritu; Anand, Sameer; Jaiswal, Ajay; Aggarwal, Anu G.

doi:10.1111/exsy.13264

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…As a result, we utilize the severity levels written in the bug reports as a feature to assign bug reports to developers. Predicting the severity of bugs with machine learning (ML) methods is a popular topic in literature [10,11].…”

Section: Introductionmentioning

confidence: 99%

Bug Report Classification with Ensemble Learning for Closed-Source Software

Yılmaz,

Öztürk,

Köksal

2023

Preprint

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This paper introduces a set of datasets grouped under the name Turkish Software Bug Reports (TSBR), which comprises commercial software bug reports from a closed-source project. We investigate and report the statistical properties and classification difficulty of the TSBR datasets. We employ various methods from the text classification literature to apply several classification tasks related to software development on the TSBR datasets. The methods we employ include traditional machine learning (ML) methods such as k-nearest neighbors (KNN) and random forest (RF); sequential deep learning (DL) models such as gated recurrent unit (GRU) and convolutional neural network (CNN); transformer-based language models; and ensembles of the employed models. Our work is among the first efforts in automated bug report classification literature that uses ensembles of DL models.

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Section: Introductionmentioning

confidence: 99%

Bug Report Classification with Ensemble Learning for Closed-Source Software

Yılmaz,

Öztürk,

Köksal

2023

Preprint

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“…The authors Ritu Bibyan et al (2023) in their study have proposed 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 were collected from the Eclipse project using convolutional neural network (CNN).…”

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confidence: 99%

Special issue on International conference on computing and communication networks (ICCCN2022)

Gupta

2023

Expert Systems

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The past years have seen a flurry of activity in distributed computing and its related fields. Indeed, developments in computer communications and networks enabled the deployment of exciting new areas, including internet of things, vehicular networks, collaborative big data analysis and so on. The design and implementation of energy efficient future generation communication and networking technologies fostered the development of mobile, pervasive and large-scale computing technologies.The International Conference on Computing and Communications Networks (ICCCN 2022) aims to serve as a forum for exchanging the latest findings and experiences ranging from theoretical research to practical system development in all aspects of computing and networking. All submitted papers must have a substantial knowledge engineering component like AI/machine learning, etc. to be in scope of this special issue, otherwise the paper will get rejected straightaway. This special issue brings selected papers presented at the ICCCN 2022 conference. From around 45 submitted articles, only 12 papers were selected based on the reviews. Each paper was reviewed by at least two reviewers and went through at least two rounds of reviews. The brief contributions of these papers are discussed below.In the first paper of this special issue, the authors Minni Jain et al. ( 2023) have proposed the first approach for code-mixed Hindi-English social media text that comprises language identification, detection and correction of non-word (out of vocabulary) errors as well as real-word errors occurring simultaneously. A fuzzy graph between different words of the suggestive lists is generated using various semantic relations in Hindi WordNet. Word embeddings and fuzzy graph-based centrality measures are used to find the correct word. Several experiments are performed on different social media datasets taken from Instagram, Twitter, YouTube comments, Blogs and WhatsApp. The experimental results demonstrate that the proposed system corrects out-of-vocabulary words as well as real-word errors with a maximum recall of 0.90 and 0.67, respectively, for Dev_Hindi and 0.87 and 0.66, respectively, for Rom_Hindi. The authors Ritu Bibyan et al. (2023) in their study have proposed a prediction model based on LDA to study the content aspect and emotion

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