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.

• Publication Date: 27-Aug-2014
• DOI: 10.15224/978-1-63248-024-8-14
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Abstract: Forward Error Correction is an essential requirement for wireless communication systems with high bit error rates. Redundant Residue Number System codes are normally superior in parallel communication environments, such as sensor networks due to their weightless structure. Underlying error correction capability of redundant residue representation has led to the development of a new set of coding schemes. In this research, a novel error control technique, based on the residue number system is proposed and implemented using MATLAB. With the design of a new minimum-Hamming distance decoder, the proposed system achieved a more efficient error correction ability compared to the Reed Solomon code, particularly in lower signal to noise ratios.

• Publication Date: 27-Aug-2014
• DOI: 10.15224/978-1-63248-024-8-15
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