Assessing the Severity of Software Bug Using Neural Network

Bibyan, Ritu; Anand, Sameer; Jaiswal, Ajay

doi:10.1007/978-981-15-3647-2_35

Cited by 5 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A survey was conducted recently based on a few papers from the year 2010–2017, it was observed that researchers have used various machine learning‐based classification algorithms for severity prediction models (Ramay et al, 2019). Some of the algorithms used are k‐Nearest Neighbour (Tian et al, 2012), SVM (Lamkanfi et al, 2011), Random Forest (Bengio, 2009), and Ensemble methods like bagging (Bibyan et al, 2020). The survey also claimed that more than 25 types of features have been studied so far.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…e main focus of software maintenance is to resolve the bug reported by users and testers of the system when it is tested or used [1,6]. In software development life cycle, 90% software's cost is spent during the phase of software maintenance.…”

Section: Introductionmentioning

confidence: 99%

“…Software engineering is the main discipline in which high quality software is developed by applying user-centered design principles. Different tasks and operations of an organization are handled by software systems [ 1 ]. The new IoT technology is gaining attention with these healthcare systems for medical filed, and IoT devices such as sensors and smart equipment are using for the treatment of the patients [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Defect prediction techniques are used in software maintenance process to improve the reliability of software by isolating a bug. The main focus of software maintenance is to resolve the bug reported by users and testers of the system when it is tested or used [ 1 , 6 ]. In software development life cycle, 90% software's cost is spent during the phase of software maintenance.…”

Section: Introductionmentioning

confidence: 99%

“…Motivation : Machine learning approaches are introduced to overcome the burden of manually assigning severity levels. The multiple ML approaches such as ensemble techniques, comparison of different algorithms such as NB, RF, KNN, MLR, J48, and RNG, CNN and SMOTE for severity assignment is performed [ 1 , 15 , 16 ]. However, these approaches do not consider proper feature selection methods [ 17 ] and emotion-based technique [ 6 ] and so on.…”

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

View full text Add to dashboard Cite

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.

show abstract