Abstract
There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on order statistics or Non-Homogeneous Poisson Processes (NHPP), with asymptotic confidence levels for interval estimates of parameters. In particular, interval estimates from these models are obtained for the conditional failure rate of the software, given the data from the debugging process. The data can be grouped or ungrouped. For someone making a decision about when to market software, the conditional failure rate is an important parameter. Order statistics are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many books. Statistical Process Control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper we proposed a control mechanism based on order statistics of cumulative quantity between observations of time domain failure data using mean value function of Half Logistics Distribution (HLD) based on NHPP.
Highlights
The monitoring of Software reliability process is a far from simple activity
It is reported that Statistical Process Control (SPC) can be successfully applied to several processes for software development, including software reliability process
The sum total in each subgroup would denote the time lapse between every 5th order statistics in a sample of size 5.In general for inter-failure data of size ‘n’, if r less than ‘n’ and preferably a factor n, we can conviently divide the data into ‘k’ disjoint subgroups (k=n/r) and the cumulative total in each subgroup indicate the time between every rth failure
Summary
The monitoring of Software reliability process is a far from simple activity. In recent years, several authors have recommended the use of SPC for software process monitoring. Our effort is to apply SPC techniques in the software development process so as to improve software reliability and quality [2]. It is accepted on all hands that Statistical process control acts as a powerful tool for bringing about improvement of quality as well as productivity of any manufacturing procedure and is relevant to software development . Viewed in this light, SPC is a method of process management through application of statistical analysis, which involves and includes the defining, measuring, controlling, and improving of the processes [5]
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