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A study of the exponential smoothing technique in software reliability growth prediction
Author(s) -
Xie M.,
Hong G. Y.,
Wohlin C.
Publication year - 1997
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/(sici)1099-1638(199711/12)13:6<347::aid-qre116>3.0.co;2-o
Subject(s) - exponential smoothing , computer science , reliability engineering , software quality , software , parametric statistics , reliability (semiconductor) , smoothing , software reliability testing , process (computing) , focus (optics) , parametric model , data mining , software development , engineering , statistics , mathematics , computer vision , programming language , power (physics) , physics , operating system , optics , quantum mechanics
Software reliability models can provide quantitative measures of the reliability of software systems which are of growing importance today. Most of the models are parametric ones which rely on the modelling of the software failure process as a Markov or non‐homogeneous Poisson process. It has been noticed that many of them do not give a very accurate prediction of future software failures as the focus is on the fitting of past data. In this paper we study the use of the double exponential smoothing technique to predict software failures. The proposed approach is a non‐parametric one and has the ability of providing more accurate prediction compared with traditional parametric models because it gives a higher weight to the most recent failure data for a better prediction of future behaviour. The method is very easy to use and requires a very limited amount of data storage and computational effort. It can be updated instantly without much calculation. Hence it is a tool that should be more commonly used in practice. Numerical examples are shown to highlight the applicability. Comparisons with other commonly used software reliability growth models are also presented. © 1997 John Wiley & Sons, Ltd.