Statistical Estimation of Software Reliability.

When a new computer software package is developed, a testing procedure is often put into effect to eliminate the faults, or bugs, in the package. One common procedure is th try the package on a set of well-known problems to try to see if any errors result. This goes for some fixed time with all detected errors being noted. Then the testing stops and the package is carefully checked to determine the r specific bugs that were responsible for the observed errors, and the package is then altered to remove these bugs. A problem of great importance is the estimation of the error rate of this revised software package. To model the above, we suppose that initially the package contains m, an unknown number of bugs which cause errors to occur in accordance with independent Poisson process having unknown rates λi, i = 1, …, m. We suppose that the package is to be run for t time units and that each error is, independently, detected with some known probability p. At the end of this time, a careful check of the package is made to determine the specific bugs that caused the detected errors (that is, a debugging takes place). These bugs are then removed and the problem of interest is to determine the error rate for the revised package. In this paper, we show how to estimate this quantity under a variety of assumptions as to what is learned when the debugging occurs. [ABSTRACT FROM AUTHOR]