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 Black Box Cyan paint color changes from yellow to
green paint.
4.
Definition: A Single Input is red, and a single Output is red.
Analysis: The Output implies the nonexistence of color among system
resources. Besides, no colors modify the red color among system resources within Black Box.
5.
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 in multiple test occasions are used as Inputs, and the Output
does not give any colors.
Analysis: When the outcome has no color, it implies 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.
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 to an entity or entities in system resources,
which decreases Input to half value and returns it to an Output value.
11.
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.
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.
Analysis: (Green paint/2) implies to an entity or entities, which decrease
the result to a half amount in system resources. Half the volume of Input (the half
volume of yellow paint) exists in a system environment. A half amount of Input
(half 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 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 into 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 enters
through an oral channel in a physiological framework. Water interacts and mixes
with several components in Biological Systems. Water can absorb Biological
Components, and Excess Water can evacuate.
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:
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:
It requires manipulative Inputs when
functional Outputs associate with threads of Local Instincts in Biological
Systems. Manipulative Inputs demand statistical analysis for practical outputs.
Observation:
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 performances.
Observation:
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.
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