Software Failure Time Data Analysis via Wavelet-Based Approach

2012 ◽  
Vol E95.A (9) ◽  
pp. 1490-1497 ◽  
Author(s):  
Xiao XIAO ◽  
Tadashi DOHI
Keyword(s):  
Data Analysis ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Software Failure

DYNAMIC SOFTWARE RELIABILITY PREDICTION: AN APPROACH BASED ON SUPPORT VECTOR MACHINES

2005 ◽  
Vol 12 (04) ◽  
pp. 309-321 ◽  
Author(s):  
LIANG TIAN ◽  
AFZEL NOORE
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Failure Time ◽  
Reliability Prediction ◽  
Support Vector ◽  
Failure Time Data ◽  
Time Data ◽  
Software Failure ◽  
Learning Scheme ◽  
Software Reliability Prediction

A support vector machine (SVM) modeling approach for software reliability prediction is proposed. Based on the structural risk minimization principle, the learning scheme of SVM is focused on minimizing an upper bound of the generalization error that eventually results in better generalization performance. The SVM learning scheme is applied to the failure time data, forcing the network to learn and recognize the inherent internal temporal property of software failure sequence. Further, the SVM learning process is iteratively and dynamically updated after every occurrence of new failure time data in order to capture the most current feature hidden inside the software failure behavior. The performance of our proposed approach has been tested using four real-time control and flight dynamic application data sets and compared with feed-forward neural network and recurrent neural network modeling approaches. Experimental results show that our proposed approach adapts well across different software projects, and has a better next-step prediction performance.

A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

2021 ◽  
pp. 096228022110092
Author(s):  
Mingyue Du ◽  
Hui Zhao ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Significant Feature ◽  
Failure Time Data ◽  
Time Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Cox's Proportional Hazards Model

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

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