Severity Prediction for Bug Reports Using Multi-Aspect Features: A Deep Learning Approach

Dao, Anh-Hien; Yang, Chengtao

doi:10.3390/math9141644

Cited by 12 publications

(6 citation statements)

References 47 publications

(95 reference statements)

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“…The construct validity is the selection of the evaluation metrics that evaluate the performance of the proposed approach. We have used accuracy, precision, recall, and f1-measure that are mostly used as evaluation metrics [ 6 , 13 ]. We have worked with these metrics, and results are promising, if we work with different evaluation metrics like AUC, then results can vary.…”

Section: Experimental Results and Findingsmentioning

confidence: 99%

“…Multiattribute-centered classification and regression model is proposed for the prediction of severity and bug fix time. Sharma et al [32] [13]. For multiclass severity classification, BCR approach is proposed based on CNN and RF with Boosting [6].…”

Section: Machine Learning For Bug Severity Prediction For General Sof...mentioning

confidence: 99%

“…Multiaspect feature approach is proposed for feature extraction on Eclipse and Mozilla dataset that feed into the convolutional neural network algorithm. Average results are accuracy 75%, precision 78%, F1-measure 86%, and MCC 41%, respectively [ 13 ]. For multiclass severity classification, BCR approach is proposed based on CNN and RF with Boosting [ 6 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bug severity can also be assigned using ML algorithms. Literature has defined multiple levels of severity such as severe and nonsevere [ 6 , 13 ], blocker, critical, major, minor, and trivial along with addition of normal and enhancement level [ 14 ]. User assigns the level of severity when they report the bug, and it causes an inappropriate assessment of severity due to inexperienced user and lack of domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

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An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

Yousaf

Anwar

Imtiaz

et al. 2022

Computational Intelligence and Neuroscience

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Softwares are involved in all aspects of healthcare, such as booking appointments to software systems that are used for treatment and care of patients. Many vendors and consultants develop high quality software healthcare systems such as hospital management systems, medical electronic systems, and middle-ware softwares in medical devices. Internet of Things (IoT) medical devices are gaining attention and facilitate the people with new technology. The health condition of the patients are monitored by the IoT devices using sensors, specifically brain diseases such as Alzheimer, Parkinson’s, and Traumatic brain injury. Embedded software is present in IoT medical devices and the complexity of software increases day-by-day with the increase in the number and complexity of bugs in the devices. Bugs present in IoT medical devices can have severe consequences such as inaccurate records, circulatory suffering, and death in some cases along with delay in handling patients. There is a need to predict the impact of bugs (severe or nonsevere), especially in case of IoT medical devices due to their critical nature. This research proposes a hybrid bug severity prediction model using convolution neural network (CNN) and Harris Hawk optimization (HHO) based on an optimized hyperparameter of CNN with HHO. The dataset is created, that consists of the bugs present in healthcare systems and IoT medical devices, which is used for evaluation of the proposed model. A preprocessing technique on textual dataset is applied along with a feature extraction technique for CNN embedding layer. In HHO, we define the hyperparameter values of “Batch Size, Learning Rate, Activation Function, Optimizer Parameters, and Kernel Initializers,” before training the model. Hybrid model CNN-HHO is applied, and a 10-fold cross validation is performed for evaluation. Results indicate an accuracy of 96.21% with the proposed model.

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Section: Experimental Results and Findingsmentioning

confidence: 99%

Section: Machine Learning For Bug Severity Prediction For General Sof...mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

Yousaf

Anwar

Imtiaz

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…Their approach involves combining the word2vec [11] layer with multiple fully-connected network layers. Building upon this work, Dao et al [35] incorporated multi-aspect features in bug reports and utilized a CNN-based model to make predictions. Similarly, Agrawal et al [9] trained a word2vec architecture on bug report corpus to predict bug severity.…”

Section: Related Work a Bug Severity Predictionmentioning

confidence: 99%

Improving Bug Severity Prediction With Domain-Specific Representation Learning

Ye¹,

Zhang

Ren

2023

IEEE Access

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Automating the process of bug severity assignment can accelerate bug triagers' efficiency in the life-cycle of software maintenance, improving the quality of software products. The mainstream approaches for bug severity prediction mainly use different neural networks due to their automated learning ability. However, there are two problems that make existing approaches fail to predict severities for some bugs: 1) they cannot learn the internal knowledge of bug reports; 2) supervised training is difficult to understand the global context of bug reports. To resolve these two problems, in this paper, we propose a bug severity prediction approach, namely KICL, which combines pre-trained language models and domain-specific pre-training strategies, i.e., Knowledge-Intensified pre-training and contrastive learning pre-training. Specifically, Knowledge-Intensified allows KICL to learn project-specific bug report tokens, deeply understanding internal knowledge of bug reports. As for contrastive learning, it allows KICL to perform sequence-level learning, understanding bug reports from the perspective of the global context. When finishing pre-training, we can fine-tune pre-trained KICL for bug severity prediction. To evaluate the effectiveness of KICL, we choose six baseline approaches and compare their performance on a public dataset. The experimental results show that KICL outperforms all baseline approaches by up to 30.68% in terms of weighted average F1-score, achieving new results for bug severity prediction.INDEX TERMS Bug severity prediction, domain-specific pre-training, software maintenance, pre-trained language model.

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Software Bug Severity Prediction Using Convolutional Neural Network and BiLSTM Models

Mian,

Alsaeedi

2024

Lecture Notes on Data Engineering and Communications Technologies

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Severity Prediction for Bug Reports Using Multi-Aspect Features: A Deep Learning Approach

Cited by 12 publications

References 47 publications

An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

Improving Bug Severity Prediction With Domain-Specific Representation Learning

Software Bug Severity Prediction Using Convolutional Neural Network and BiLSTM Models

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