Abstract

Software reliability is one of the most important characteristics of software quality. Its measurement and management technologies employed during the software life cycle are essential for producing and maintaining quality/reliable software systems. Over the last several decades, many Software Reliability Growth Models (SRGMs) have been developed to greatly facilitate engineers and managers in tracking and measuring the growth of reliability as software is being improved. Statistical process control (SPC) is a branch of statistics that combines rigorous time series analysis methods with graphical presentation of data, often yielding insights into the data more quickly and in a way more understandable to lay decision makers. SPC has been applied to forecast the software failures and improve the software reliability. In this paper we proposed Pareto Type II Distribution model with an order statistic approach and applied SPC to monitor the failures. Also the proposed model is compared with Half Logistic Distribution considering time domain data based on Non Homogeneous Poisson Process (NHPP). The parameters are estimated using the Maximum Likelihood Estimation. The failure data is analyzed with both the models and the results are exhibited through control charts.

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