Recalibrating Software Reliability Models
IEEE Transactions on Software Engineering
The use of ARIMA models for reliability forecasting and analysis
Proceedings of the 23rd international conference on on Computers and industrial engineering
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
New Ways to Get Accurate Reliability Measures
IEEE Software
Applying Reliability Models More Effectively
IEEE Software
Using Neural Networks in Reliability Prediction
IEEE Software
Software failure prediction based on a Markov Bayesian network model
Journal of Systems and Software
Software reliability forecasting by support vector machines with simulated annealing algorithms
Journal of Systems and Software
Software Reliability Engineering: A Roadmap
FOSE '07 2007 Future of Software Engineering
Software Reliability Models: Assumptions, Limitations, and Applicability
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Software Reliability Prediction Using Wavelet Neural Networks
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
Software reliability prediction by soft computing techniques
Journal of Systems and Software
An autoregressive time series software reliability growth model with independent increment
MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
The reliability estimation, prediction and measuring of component-based software
Journal of Systems and Software
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Reliability is the key factor for software system quality. Several models have been introduced to estimate and predict reliability based on results of software testing activities. Software Reliability Growth Models (SRGMs) are considered the most commonly used to achieve this goal. Over the past decades, many researchers have discussed SRGMs' assumptions, applicability, and predictability. They have concluded that SRGMs have many shortcomings related to their unrealistic assumptions, environment-dependent applicability, and questionable predictability. Several approaches based on non-parametric statistics, Bayesian networks, and machine learning methods have been proposed in the literature. Based on their theoretical nature, however, they cannot completely address the SRGMs' limitations. Consequently, addressing these shortcomings is still a very crucial task in order to provide reliable software systems. This paper presents a well-established prediction approach based on time series ARIMA (Autoregressive Integrated Moving Average) modeling as an alternative solution to address the SRGMs' limitations and provide more accurate reliability prediction. Using real-life data sets on software failures, the accuracy of the proposed approach is evaluated and compared to popular existing approaches.