Title :
Time series analysis for bug number prediction
Author :
Wu, Wenjin ; Zhang, Wen ; Yang, Ye ; Wang, Qing
Author_Institution :
Lab. for Internet Software Technol., Chinese Acad. of Sci., Beijing, China
Abstract :
Monitoring and predicting the increasing or decreasing trend of bug number in a software system is of great importance to both software project managers and software end-users. For software managers, accurate prediction of bug number of a software system will assist them in making timely decisions, such as effort investment and resource allocation. For software end-users, knowing possible bug number of their systems will enable them to take timely actions in coping with loss caused by possible system failures. To accomplish this goal, in this paper, we model the bug number data per month as time series and, use time series analysis algorithms as ARIMA and X12 enhanced ARIMA to predict bug number, in comparison with polynomial regression as the baseline. X12 is the widely used seasonal adjustment algorithm proposed by U.S. Census. The case study based on Debian bug data from March 1996 to August 2009 shows that X12 enhanced ARIMA can achieve the best performance in bug number prediction. Moreover, both ARIMA and X12 enhanced ARIMA outperform the baseline as polynomial regression.
Keywords :
regression analysis; security of data; software quality; time series; ARIMA algorithm; X12 enhanced ARIMA algorithm; effort investment; polynomial regression; resource allocation; software end-users; software project managers; software system; time series analysis; Computer bugs; Linux; Monitoring; Polynomials; Predictive models; Programming; Project management; Resource management; Software systems; Time series analysis; ARIMA; XI2; bug number prediction; polynomial regression; time series analysis;
Conference_Titel :
Software Engineering and Data Mining (SEDM), 2010 2nd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-7324-3
Electronic_ISBN :
978-89-88678-22-0
