Numerous Software Reliability Growth Models (SRGMs) have been discussed in the
literature. These models are used to predict fault content and reliability of software. It has been observed that the relationship between testing time and the corresponding number of faults removed is either exponential or S-shaped, or a mix of the two. Another important class of SRGMs, known as flexible SRGMs, can depict both
exponential and S-shaped growth curves. The paper introduces a new concept of power logistic learning function that proves to be very flexible, in the sense that it represents various curve types – exponential, Rayleigh, Weibull or simple logistic. The flexible nature of the power logistic function gives the flexible SRGM a higher degree of accuracy and wider applicability.
Verskeie voorbeelde van Betroubaarheidsgroeimodelle vir programmatuur word in die literatuur beskryf. Die modelle word gebruik vir die voorspelling van foutinhoud
en programmatuurbetroubaarheid. Daar word waargeneem dat die verband tussen
toetstyd en die resulterende foutverwydering eksponensiaal of S-vormig of ‘n kombinasie daarvan is. Aanpasbare modelle insluitende diskrete ekwivalente word ook behandel. Die publikasie ontleed vervolgens algemene plooibare maglogistieke leerkromme met wye toepasbaarheid wat slaan op eksponensiële, Rayleigh-,
Weilbull- en logistieke funksies. Die plooibaarheid van die model waarborg
akkuraatheid en wye toepasbaarheid met die verlangde gehalte van voorspelbaarheid.
