Software reliability prediction via relevance vector regression

Lou, Jungang; Jiang, Yunliang; Shen, Qing; Shen, Zhongliang; Wang, Zhen; Wang, Ruiqin

doi:10.1016/j.neucom.2015.12.077

Cited by 29 publications

(14 citation statements)

References 26 publications

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“…The RVM introduces a prior to the weight of the model and the most probable value is iteratively estimated from the data set [7]. Given a set of examples of input vectors   1…”

Section: Reviews Of Related Theories 21 Relevance Vector Machinementioning

confidence: 99%

Remaining useful life prognostics for the electro-hydraulic actuator using relevance vector machine and optimized on-line incremental learning

2019

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The electro-hydraulic actuator plays a significant role in the automatic flight control system, so it is vital to predict the remaining useful life (RUL) for the electro-hydraulic actuators. Relevance vector machine (RVM) is flourishing in the field of RUL prognostics and gradually applied to the prediction of complex systems or components, but the general RVM cannot achieve on-line prediction efficiently due to its high computational complexity, besides, the sparse RVM model which is only based on historical data set could cause a large prediction error in the long term. To deal with these plights, an optimized incremental learning algorithm based on RVM is presented taking full advantage of the on-line updating samples to improve the precision of prognostics.

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“…The RVM introduces a prior to the weight of the model and the most probable value is iteratively estimated from the data set [7]. Given a set of examples of input vectors   1…”

Section: Reviews Of Related Theories 21 Relevance Vector Machinementioning

confidence: 99%

Remaining useful life prognostics for the electro-hydraulic actuator using relevance vector machine and optimized on-line incremental learning

2019

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“…How to manage the spectrum resources of the community as a whole, reasonably determine the communication power of each device, and minimize the interference between devices have become the main bottleneck for D2D communication to enter the practical stage. It is worthwhile to try to achieve power stability [15,16] or learn from software reliability prediction through context sensitive rate Boolean control network [17].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Full Duplex D2D Clustering Resource Allocation Scheme Based on a K‐Means Algorithm

Huang

Meng-jia

Fan

et al. 2018

Wireless Communications and Mobile Computing

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Although the Device-to-Device (D2D) technology in cellular networks can improve the performance of cellular systems, it creates a large amount of interference in traditional communications. In this paper, the problem of resource allocation and control in a single-cell scene is studied. First, the concept of a restricted D2D communication area and a restricted D2D user-reusage area is put forward to reduce the complexity and interference intensity of resource allocation. Second, under the premise of satisfying the QoS (Quality of Service) demands of every system user, the resource allocation algorithm is improved, the optimal allocation of resources is carried out, and the algorithm’s processes are given in detail. Our simulated experiments show that the proposed method greatly improves the spectrum efficiency and the system fairness.

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“…To improve software reliability [31] with limited human resource, in this paper we propose an effort-aware just-in-time defect prediction approach based on neural networks and deep learning. Deep learning has been applied to many learning tasks, such as speech recognition, image processing and code completion [32, 33].…”

Section: Introductionmentioning

confidence: 99%

Effort-aware and just-in-time defect prediction with neural network

Qiao

Wang

2019

PLoS ONE

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Effort-aware just-in-time (JIT) defect prediction is to rank source code changes based on the likelihood of detects as well as the effort to inspect such changes. Accurate defect prediction algorithms help to find more defects with limited effort. To improve the accuracy of defect prediction, in this paper, we propose a deep learning based approach for effort-aware just-in-time defect prediction. The key idea of the proposed approach is that neural network and deep learning could be exploited to select useful features for defect prediction because they have been proved excellent at selecting useful features for classification and regression. First, we preprocess ten numerical metrics of code changes, and then feed them to a neural network whose output indicates how likely the code change under test contains bugs. Second, we compute the benefit cost ratio for each code change by dividing the likelihood by its size. Finally, we rank code changes according to their benefit cost ratio. Evaluation results on a well-known data set suggest that the proposed approach outperforms the state-of-the-art approaches on each of the subject projects. It improves the average recall and popt by 15.6% and 8.1%, respectively.

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Software reliability prediction via relevance vector regression

Cited by 29 publications

References 26 publications

Remaining useful life prognostics for the electro-hydraulic actuator using relevance vector machine and optimized on-line incremental learning

Remaining useful life prognostics for the electro-hydraulic actuator using relevance vector machine and optimized on-line incremental learning

A Full Duplex D2D Clustering Resource Allocation Scheme Based on a K‐Means Algorithm

Effort-aware and just-in-time defect prediction with neural network

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