The Black Box
Testing Model explores a metaphoric reflection to identify complex parameters
within Isolated Systems. Therefore, the following illustrations describe simple
patterns of the Black Box's thinking and functioning.
Standard Data Processing for Input /Output
Devices
Simple Black-Box analysis for Non-Biological Systems through a testing combination of color patterns
1.
Definition: A Single Input is red, and a single
Output is yellow.
Analysis: The Output implies the existence of
a green color among system resources within the Black Box, and the green color
changes from red to yellow.
2.
Definition: A Single Input is blue in the
Black-box, and then a single Output is white.
Analysis: The Output implies yellow among
system resources within the Black Box, and the blue color changes from a yellow
tint to white.
3.
Definition: Single Input is the yellow paint
color, and a single Output is the green paint.
Analysis: The Output implies the existence of
cyan paint color among system resources within the Black Box. Cyan paint color
changes from yellow to green.
4.
Definition: A Single Input and a single Output
are red.
Analysis: The Output implies the nonexistence
of color among system resources. Besides, no color modification was made to the
red color among the system resources within Black Box.
5.
Definition: Single Input is red, and the result
does not show colors.
Analysis: The nonexistence of color shows up
in the outcome. It implies an entity among system resources, sometimes
eliminating the red color.
Observation:
Multiple tests for
other occasions can indicate different color context conditions. It implies that
broad colors among system resources eliminate red.
6.
Definition: Single colors on multiple test occasions are inputs; the output gives no
colors.
Analysis: When the outcome has no color, it
implies that an entity or entities among system resources eliminate Inputs.
7.
Definition: Single Input is red, and the
unexpected color combinations are Outputs.
Analysis: The unexpected color combinations in
Outputs can imply parameter complexity in the Black Box testing process or
complexity in the system platform.
Solution: detect
algorithm complexity and data structure in the system platform.
8.
Definition: Single Input is yellow, and (Yellow)2
indicates the result.
Analysis: (Yellow)2 implies an
entity or entities in system resources, and the Input value can represent
double values in the result.
9.
Definition: Single Input is yellow, and (Yellow)N
is Output.
Analysis: (Yellow)2 implies an
entity or entities, which increases Input N times in system resources. There is
a complexity risk within the Black Box because the Outputs become multiple
copies of the Input, which requires determining risk assessment tests.
10.
Definition: Single Input is yellow, and (Yellow/
2) indicates the result.
Analysis: (Yellow/2) implies an entity or
entities in system resources, which decreases the Input to half the value and
returns it to an Output value.
11.
Definition: Single Input is the yellow paint
color, and (Green paint)2 indicates the outcome.
Analysis: (Green paint)2 implies an
entity or entities in system resources, increasing Input volume twice and
returning it to the product. Cyan and yellow paint colors exist among system
resources in the Black Box.
12.
Definition: Single Input is the yellow paint
color, and (Green paint) N indicates the outcome.
Analysis: (Green paint) N implies
that an entity increases Output volume N times through system resources. An
aggregate of cyan and yellow paint colors exists among system resources in the
Black Box.
13.
Definition: Single Input is the yellow paint
color, and (Green paint/2) indicates the outcome.
Analysis: (Green paint/2) implies an entity or
entities, which decreases the result by a half amount in system resources. Half
the volume of Input (the half volume of yellow paint) exists in a system
environment. Half of the Input (half the amount of cyan color) exists in the
Black Box before entering Input data in a system environment.
Simple Black-Box
Analysis for Biological Systems
The Black Box can be
either a brain (logical framework) or a physiological framework for Biological
Systems. When the Black Box is a Brain framework, data Input channels aim at the
five senses: seeing, smelling, hearing, touching, and tasting. Functional
interference on the part of the brain framework interprets Inputs and chooses
certain sensations over others. Eventually, the decision is based on multiple
Inputs, which gives more attention. Brain framework functions across domains of
cognition and generates Output behaviors for Biological Systems.
Biological Systems
have two main alternative channels of outcomes, which underlay a trade-off
between physiological and behavioral mechanisms. Behavioral Mechanisms generate
Outputs through logical data in Brain reflections, and Physiological Mechanisms
create Outputs through the coordination of Physical Resources and the Brain,
according to the following illustrations:
1-Behavioral Mechanisms
2-Physiological Mechanisms
Definition: Drinking a cold glass of water through
an oral channel. The water is the Input Object. The result is either absorbed
in Biological Components or evacuated through Output channels.
Analysis: A photo of a cold glass of water
stimulates a thirsty man to drink.
Input is entered
through an oral channel in a physiological framework. Water can absorb
biological components, and excess water can be evacuated.
Definition: Water is the Input, and urine is the outcome within
physiological Systems.
Analysis: Normal urine color ranges from pale yellow to deep
amber. Unusual urine color is among the most common signs of a urinary tract
infection. Urine color can show health risk factors in Biological
Systems.
Observation 1:
It is easy to assume
the model functional Outputs in Biological Systems when Inputs are associated
with instance threads of Global Instincts in Biological Systems. It indicates
which Instincts are activated or inactive in the Black Box (Brain framework).
Social behaviors reveal algorithmic parameters in the Instinct Component.
Observation 2:
It requires
manipulative inputs when functional outputs are associated with threads of
local instincts in Biological Systems. Manipulative Inputs demand statistical
analysis for practical outputs.
Observation 3:
Isolation and
separation strategies are some of the offensive tactics that Systems Owners
impose against opponents. The Isolation strategy is a cost-effective and
suboptimal tactic in the short term; however, it generates Invisible Entities
and side effects on the evolutionary path of system performance.
Observation 4:
Unpredictable
incidents, either within internal or external Isolated Systems, can reflect
algorithmic patterns within Isolated Systems. Eventually, the system controller
can measure security models within Isolated Systems.
Simple Black-Box
Analysis for Non-Biological Systems
The broad practical
implication of the Black Box principle can apply to intervention either in
Complex Systems or Isolated Systems. Identifying the internal resource property
of opponents' platforms can enhance competitiveness. It ensures effective decision-making
about opponents' platforms. An opponent can be an Isolated System.
