A Nonlinear ARIMA Technique for Debian Bug Number Prediction

JAYADEEP, PATI and K.K., SHUKLA (2014) A Nonlinear ARIMA Technique for Debian Bug Number Prediction. In: International Conference on Advances in Computer and Electronics Technology - ACET 2014, 26 - 27 August, 2014, City University of Hongkong, Hongkong.

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Abstract

A bug in a software application may be a requirement bug, development bug, testing bug or security bug, etc. To predict the bug numbers accurately is a challenging task. Both end users and software developers get benefit by predicting the number of bugs in a new version of software application in advance. The choice of predicting models becomes an important factor for improving the prediction accuracy. This paper provides a combination methodology that combines ARIMA and ANN models for predicting the bug numbers in advance. This method is examined using bug number data for Debian which is publicly available. This paper also gives a comparative analysis of forecasting performance of hybrid Nonlinear ARIMA, ARIMA and ANN models. Empirical results indicate that an Nonlinear ARIMA model can improve the prediction accuracy.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Debian, Bug, Bug Pattern, Artificial Neural Network, ARIMA, Hybrid Model.
Depositing User: Mr. John Steve
Date Deposited: 28 May 2019 09:27
Last Modified: 28 May 2019 09:27
URI: http://publications.theired.org/id/eprint/2871

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