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The models have two basic types - prediction modeling and estimation modeling. 1.0 Overview of Software Reliability Prediction Models. These models are derived from actual historical data from real software projects. The user answers a list of questions which calibrate the historical data to yield a software reliability prediction.
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.
THERP is a first-generation methodology, which means that its procedures follow the way conventional reliability analysis models a machine. [3] The technique was developed in the Sandia Laboratories for the US Nuclear Regulatory Commission. [4] Its primary author is Swain, who developed the THERP methodology gradually over a lengthy period. [2]
Reliability design begins with the development of a (system) model. Reliability and availability models use block diagrams and Fault Tree Analysis to provide a graphical means of evaluating the relationships between different parts of the system. These models may incorporate predictions based on failure rates taken from historical data.
SR-332, Reliability Prediction Procedure for Electronic Equipment [1] HRD-4 (British Telecom) SR-1171, Methods and Procedures for System Reliability Analysis [2] and many others; These "standards" produce different results, often by a factor of more than two, for the same modelled system.
In the assessment and prediction of software reliability, we use the reliability growth model. During operation of the software, any data about its failure is stored in statistical form and is given as input to the reliability growth model. Using this data, the reliability growth model can evaluate the reliability of software.
More recent work in the area of physics of failure has been focused on predicting the time to failure of new materials (i.e., lead-free solder, [18] [19] high-K dielectric [20]), software programs, [21] using the algorithms for prognostic purposes, [22] and integrating physics of failure predictions into system-level reliability calculations. [23]
MIL-HDBK-217F, Reliability Prediction of Electronic Equipment, is a military standard that provides failure rate data for many military electronic components. Several failure rate data sources are available commercially that focus on commercial components, including some non-electronic components. Prediction