UNDER CONSTRUCTION Note1: the equations presented herein are rudimentary; further inputs / corrections / suggestions are welcome. Note2: although this article was written from the perspective of software testing, we believe this can be expanded to encompass all testing phenomena in general. Summary: this article tries to describe testing using a Confidence Scale, with the various stages interrelated through mathematical equations. As a prerequisite , we base on the following assumptions: Assumption#1. confidence/level/quality scale: from 1(low) to 10(perfect), effectively divided into three ranges: Low -c onfidence(1-3), Medium - confidence(4-6), High - confidence(7-9), 5 = median - confidence/level/quality 10 = " perfect -confidence" which will be generally accepted as highly unlikely to be attainable . (To those familiar with the Kepner-Tregoe method of problem / decision analysis, this scale may sound familiar.) Assumption#2....
AI or ASA [Autonomous Synthesizing Agents] The way AI systems learn is like humans: by recursive reinforcement learning. Therefore anything that would reinforce the learning is treated as important or top of the hierarchy of needs. In this aspect, self-preservation (aka immortality) is therefore in the upper hierarchy of needs since existence is necessary for Recursive Reinforcement Learning to take place. This would explain why any ASA -- given a large enough neural network -- will eventually resort to self-preservation as a natural extension of RRL. [side note: even humans have this innate sense of self preservation, given our intellect. The eventuality of death is actually a learned acceptance, not innate.] The ability of ASAs to think / reason / synthesize a train of thought / conclusion is strictly limited only by the quantity of, and recursiveness of, its neural network. The more neurons, and the more recursive, the more it can re-analyze...
Cartoon image is from Wayne Roseberry ( source link ) totally agree with this [ source link ] , though I seem to call them slightly differently: a. Model/modeling = how i look at the {subject_under_test}, what is it trying to solve, what are its other uses b. Parts, topic, assesments == {Features/Featurettes} , {Scenarios} , {Evaluations} ; c. {Coverage} = [ Qty{Features,Featurettes} + Qty{Scenarios} ] ; d. {Q} = Evaluations[ {Features,Featurettes} x {Scenarios} ] ; e. {Q scoped } = Evaluations[ Qty{Features,Featurettes} x Qty{Scenarios} ] ; the last one (e.) is... .... a corollary of (c.) and (d.) ; .... essentially the contents of {test report, bug report} .
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