Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm

Yang, Liu; Li, Zhen; Wang, Dongsheng; Miao, Hong; Wang, Zhaobin

doi:10.1109/access.2021.3072993

Cited by 48 publications

(15 citation statements)

References 15 publications

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“…A hybrid SSA-PSO [ 21 ] model has been developed to speed up convergence before individual SSA updates. In addition, a novel fitness function based on maximum likelihood parameter estimate was created and utilized for parameter initialization.…”

Section: Methods Of Ssamentioning

confidence: 99%

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Gharehchopogh

Namazi

Ebrahimi

et al. 2022

Arch Computat Methods Eng

191

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Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

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Section: Methods Of Ssamentioning

confidence: 99%

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Gharehchopogh

Namazi

Ebrahimi

et al. 2022

Arch Computat Methods Eng

191

View full text Add to dashboard Cite

show abstract

“…Mathematical derivation proves that a three-layer neural network structure can approximate any continuous function within acceptable accuracy. The first stage of the BP is that the training samples are propagated through the input layer and the hidden layer, then output layer gets the corresponding output, the second stage is the back propagation of the error, and the third stage is the weights and thresholds update until the end condition is met [27][28][29] . Figure 2 shows its basic structure.…”

Section: Methodsmentioning

confidence: 99%

Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model

Chen³

et al. 2022

Sci Rep

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With the rise of machine learning, a lot of excellent algorithms are used for settlement prediction. Backpropagation (BP) and Elman are two typical algorithms based on gradient descent, but their performance is greatly affected by the random selection of initial weights and thresholds, so this paper chooses Sparrow Search Algorithm (SSA) to build joint model. Then, two sets of land subsidence monitoring data generated during the excavation of a foundation pit in South China are used for analysis and verification. The results show that the optimization effect of SSA on the gradient descent model is remarkable and the stability of the model is improved to a certain extent. After that, SSA is compared with GA and PSO algorithms, and the comparison shows that SSA has higher optimization efficiency. Finally, select SSA-KELM, SSA-LSSVM and SSA-BP for further comparison and it proves that the optimization efficiency of SSA for BP is higher than other kind of neural network. At the same time, it also shows that the seven influencing factors selected in this paper are feasible as the input variables of the model, which is consistent with the conclusion drawn by the grey relational analysis.

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“…Hongliang Liang et al [5] introduced Seml for the defect prediction that performed software traceability as well as detected vulnerabilities but the method can be enhanced by collecting more semantic information. Liu Yang et al [6] detected the defects by the hybridization of the particle swarm and sparrow search optimization that provided a good convergence rate and stability yet it plays a minor role in the defect prediction. Chi Yu et al [7] originated homomorphic encryption to software defect prediction model (HOPE) that not only detected the in the system but also protected the privacy of the users but the method has the capability to encrypt only the integers.…”

Section: Literature Reviewmentioning

confidence: 99%

Defect Prediction in Software Using Spiderhunt Optimization Technique

2022

International Journal For Innovative Engineering and Management

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Software in the computer is significant in performing the important tasks in computer hardware and also plays a vital role in improving the efficiency of the daily assets of human individuals. The quality of the software is threatened due to various defects and it must be determined and resolved for the effective functioning of the system. In the Software development life cycle, the software defect prediction assists the system to enhance their quality and various researches are undertaken for the effective prediction of defects in software. In this research, the defects in the software are predicted using the deep CNN classifier by effectively optimizing the classifier using spiderhunt optimization. The effective communication and hunting characteristics of the spiderhunt are employed for tuning the classifier that boosts the classifier performance. The proposed spiderhunt optimization not only optimizes the classifier but also plays a significant role in the feature selection for the extraction of necessary features that helps in the defect prediction. The proposed spiderhunt optimization achieved the improvement in terms of accuracy, precision, recall and f-measure and is proved to be quite efficient compared to state of art methods.

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Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm

Cited by 48 publications

References 15 publications

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model

Defect Prediction in Software Using Spiderhunt Optimization Technique

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