Quality Advocacy \___ 3rd tier/level --- designers, coders \___ 2nd tier/level --- quality advocates \___ 1st tier/level --- grassroots testers ================= definitions: ================= ..Quality [ Q ] = the state of the product (high or low) at any given time (m). ..QA [ Quality Advocacy ] = the monitoring/measuring of quality to know quality ; unless it is monitored/measured, quality [ Q ] cannot be known. Quality Advocacy extends to all 3 tiers/levels. ..QA [ Quality Assurance ] = working together as a Development Team (coders + testers + proj.mgr + designers + etc.) which will always deliver a specific level of quality. ..software tester = 1st level, grassroots interactor [someone...
How much time should be allotted to each level of test activity (unit test, integration test, system test, end-to-end test)? it varies; but definitely just enough to get the job done to an agreed level of Acceptance Credence. A model that is not adaptable to the task at hand constricts development, and needlessly constricts the actors themselves(coder, tester) -- such that, either (a) the team starts to get coerced to follow the model, sacrificing any innate efficiency; or (b) the team ignores the model and lets the nature of the task dictate the most efficient work flow. The nature of the test_paths themselves would provide the impetus to place emphasis on either (c) integration tests (e.g. majority of the test_paths are integration subsystems); (d) a balance of unit tests and end-to-end tests (e.g. the product is simple, or a self-contained supersystem); or (e) a balance of unit, integration, and comprehensive systemic tests ...
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...
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