Software Reliability (update)

Software reliability models are used to describe the evolution of the software debugging process and to measure the quality of the software. In software testing, we execute the software with a test suite that resembles the anticipated usage proole. We observe failures (unacceptable performance) caused by bugs (faults) in the program. The bugs are largely due to errors in requirement speciication, design, or coding. This entry updates that in ESS Volume 8, pp. 560-564, by Singpurwalla. Given the growing economic importance of software development, software reliability research has continued to be active and expanding. This article concentrates on developments since 1985. Various techniques are employed to detect and to correct the bugs in software, the quality of which is therefore expected to improve. We continue testing until the software reliability achieved is at a level according to speciications, or subject to time or cost constraints. Various stochastic models have been used to monitor the software quality changes due to debugging. The objectives of using these models include predicting the mean time between failures, estimating the number of residual faults, and assessing the software reliability. We roughly classify these models into two types: dynamic models and static models. Dynamic models, also called software reliability growth models, follow the changes of the software throughout the entire testing period. Most of the models employed in software reliability are dynamic models. Software reliability is deened to be the probability of failure-free operation of a computer program in a speciied environment for a speciied period of time. Most models assume that when a bug is detected in the software, it is immediately xed and the time for xing it is negligible. Perhaps it is believed that the down time provides little information about the software reliability. Dynamic models can be further divided into time domain models and counting process* models. The former are probability models for the sequence of interfailure times that are caused by faults. Many of the time domain models can be related to their dual, the counting process that counts the number of failures found in testing. Nonhomogeneous Poisson point processes* (NHPP) for modeling have been used extensively for the counting process. Static models usually measure the quality of the software by taking one snapshot. It can be taken at the beginning of the debugging phase to assist managers in planning or at the 1