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Software reliability growth (or estimation) models use failure data from testing to forecast the failure rate or MTBF into the future. The models depend on the assumptions about the fault rate during testing which can either be increasing, peaking, decreasing or some combination of decreasing and increasing.
For example, a common specification for PATA and SATA drives may be an MTBF of 300,000 hours, giving an approximate theoretical 2.92% annualized failure rate i.e. a 2.92% chance that a given drive will fail during a year of use. The AFR for a drive is derived from time-to-fail data from a reliability-demonstration test (RDT). [3]
For reliability testing, data is gathered from various stages of development, such as the design and operating stages. The tests are limited due to restrictions such as cost and time restrictions. Statistical samples are obtained from the software products to test for the reliability of the software.
The degree to which each high-level task is broken down into lower-level tasks is dependent on the availability of HEPs for the successive individual branches. The HEPs may be derived from a range of sources such as the THERP database; simulation data; historical accident data, and expert judgment. PSFs should be incorporated into these HEP ...
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
Reliability prediction can be used to size spare populations. Provide necessary input to system-level reliability models. System-level reliability models can subsequently be used to predict, for example, frequency of system outages in steady-state, frequency of system outages during early life, expected downtime per year, and system availability.
Before detailed analysis takes place, ground rules and assumptions are usually defined and agreed to. This might include, for example: Standardized mission profile with specific fixed duration mission phases; Sources for failure rate and failure mode data; Fault detection coverage that system built-in test will realize
The following table defines the possible outcomes when testing multiple null hypotheses. Suppose we have a number m of null hypotheses, denoted by: H 1, H 2, ..., H m. Using a statistical test, we reject the null hypothesis if the test is declared significant. We do not reject the null hypothesis if the test is non-significant.