Using word embedding and convolution neural network for bug triaging by considering design flaws

Sepahvand, Reza; Akbari, Reza; Jamasb, Behnaz; Hashemi, Sattar; Boushehrian, Omid

doi:10.1016/j.scico.2023.102945

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…The approach also takes into account the developers' schedule to determine the precise assignment date, resulting in a more efficient and effective bug resolution process. Sepahvand et al [38] proposed a novel approach for predicting the need to assign a bug to a designer. They developed a convolutional neural network (CNN)-based model that learned the unique characteristics of bug reports that contribute to the creation of bad smells in the code.…”

Section: Automatic Bug Assignment Using Deep Learning Techniquesmentioning

confidence: 99%

On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments

Dipongkor¹,

Islam

Hussain

et al. 2023

IEEE Access

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Automated bug report assignment is critical for large-scale software projects where reported bugs are frequent and expert developers are required to fix them on time. Finding an appropriate developer with the necessary skill sets and prior experience in fixing similar bugs is difficult and can be an expensive process, depending on the severity of the reported bug. To address this issue, researchers have proposed several machine learning and deep learning-based automated bug report assignment techniques that make use of historical data on reported bugs as well as fixer information. However, there is still room for improvement in the performance of these techniques. In this paper, we propose a novel deep learning-based approach that utilizes two sets of features from the reported bugs' textual data, namely contextual information and the occurrence of repeating keywords. We develop convolutional neural network and artificial neural network modules to mine these features. We then fuse these two sets of extracted features to assign a bug to an appropriate developer. We conduct extensive experiments on eight benchmark datasets of open-source, realworld software projects to assess the effectiveness of our approach. The experimental results demonstrate that our information fusion-based approach outperforms previous models and improves automated bug report assignment. Furthermore, we debug the errors of our proposed model and publish all source code so that future researchers can contribute to this problem.

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Section: Automatic Bug Assignment Using Deep Learning Techniquesmentioning

confidence: 99%

On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments

Dipongkor¹,

Islam

Hussain

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In order to solve the problems often encountered in network structure design, such as lack of generalization ability and easy interference phenomenon, the growth process of the brain and the functional and structural characteristics of the biological nervous system are simulated first, a system and genetic algorithm are used to establish genetic search and artificial growth models, and then the network structure design is characterized by large search space and long training time. Inspired from previous studies [21,22,23,24,25,26,27] that combines machine learning with formal methods, we propose a new algorithm that first decomposes a complex problem into multiple simple problems, and then groups the subgroups corresponding to each subproblem. The processing units of the model are divided into three levels: primary processing unit, auxiliary processing unit and secondary processing unit.…”

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confidence: 99%

The Verification and Testing on Reactive Robot Cooperating Systems

Pavakoamiea

2023

Preprint

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<p>In this paper, an robot parallel evolution design algorithm is proposed, based on the idea of module network, and the learning method is verified and tested as well. The learning method of collision avoidance, approach and wall switching behavior of evolutionary robots based on neural network method is optimized. The evolutionary robot can autonomously realize the behaviors of collision avoidance, movement, replication and attack. In the simulation environment of this paper, the obstacles in the environment are represented by a randomly generated rectangular region with the largest side length. The shape and location of obstacles are evenly distributed. The simulated environment has an approach target but no obstacles, and these approach targets are placed randomly and scattered in the environment. The formation of behavior is integrated with basic behaviors such as collision avoidance and approach, so that the robot is gradually enhanced based on the growth of neural network.</p>

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An Empirical Evaluation of Design Smells and Code Smells Over Multiple Versions of Software Evolution

Sharma,

Chhabra

2024

Proceedings of Fifth International Conference on Computing, Communications, and Cyber-Security

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Using word embedding and convolution neural network for bug triaging by considering design flaws

Cited by 5 publications

References 52 publications

On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments

On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments

The Verification and Testing on Reactive Robot Cooperating Systems

An Empirical Evaluation of Design Smells and Code Smells Over Multiple Versions of Software Evolution

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