What is the possible test approach for simulation system ?

A simulation system's primary responsibility is to replicate the behavior of the real system as accurately as possible. Therefore, a good place to start creating a test plan would be to understand the behavior of the real system.

- Subjective Testing:
It mainly depends on an expert's opinion. An expert is a person who is proficient and experienced in the system under test. Conducting the test involves test runs of the simulation by the expert and then the expert evaluates and validates the results based on some criteria. Advantage of this approach is that it can test those conditions which cannot be tested objectively. Disadvantage is that the evaluation of the system is based on the expert's opinion which may differ from expert to expert.
- Objective Testing:
It is mainly used in the systems where the data can be recorded while the simulation is running. This testing technique relies on the application of statistical and automated methods to the data collected.
Statistical methods are used to provide an insight into the accuracy of the simulation. These methods include hypothesis testing, data plots, principle component analysis and cluster analysis.
Automated testing requires a knowledge base of valid outcomes for various runs of simulation. The knowledge base is created by domain experts of the simulation system being tested. The data collected in various test runs is compared against this knowledge base to automatically validate the system under test. An advantage of this kind of testing is that the system can continually be regression tested as it is being developed.

Types of Simulation Systems: Dynamic, Discrete, Continuous and Social Simulation Systems

- Dynamic Simulation Systems: It has a model that accommodates for changes in data over time. This means that the input data affecting the results will be entered in to the simulation during its entire lifetime than just at the beginning. A simulation system used to predict the growth of the economy may need to incorporate changes in economic data is a good example of a dynamic simulation systems.

- Discrete Simulation Systems: These systems use models that have discrete entities with multiple attributes. Each of these entities can be in any state, at any given time, represented by the value of its attributes. The state of the system is a set of all the states of all its entities. This stage changes one discrete step at a time as events happen in the system. therefore, the actual designing of the simulation involves making choices about which entities to model. Examples include simulated battlefield scenarios, highway traffic control systems etc.

- Continuous Simulation Systems: If instead of using a model with discrete entities, we use data with continuous values, we will end up with continuous simulation.

- Social Simulation Systems: It is not a technique by itself but uses the various types of simulation described above. However, because of the specialized application of those techniques for social simulation, it deserves a special mention of its own. The field of social simulation involves using simulation to learn about and predict various social phenomenon such as voting patterns, migration patterns, economic decisions made by the general population etc.

Types of Simulation Systems: Deterministic, Stochastic, Static Simulation Systems

Simulation is widely used in many fields. Some of the applications are :
- Models of planes and cars that are tested in wind tunnels to determine the aerodynamic properties.
- It is used in computer games e.g. simCity, car games etc. This simulates the roads, people talking, playing games etc.
- War tactics that are simulated using simulated battlefields.
- Most of the embedded systems are developed by simulation software before they ever make it to the chip fabrication labs.
- Stochastic simulation models are often used to model applications such as weather forecasting systems.
- Social simulation is used to model socio-economic situations.
- It is extensively used in the field of operations research.

Simulation systems can be characterized in numerous ways depending on the characterization criteria applied. Some of them are:
- Deterministic Simulation Systems: These systems have completely predictable outcomes. If given a certain input, we can predict the exact outcome. Another feature of these systems is idem-potency which means that the results for any given input are always the same. Examples include population prediction models, atmospheric science etc.
- Stochastic Simulation Systems: These systems have models with random variables. This means that the exact outcome is not predictable for any given input resulting in potentially very different outcomes for the same input.
- Static Simulation Systems: These systems use statistical models in which time does not play any role. These models include various probabilistic scenarios which are used to calculate the results of any given input. Examples of such systems include financial portfolio valuation models.

Types of Software Systems : Diagnostic Software Systems, Sensor and Signal Processing Systems, Simulation Systems

The type of software system refers to the processing that will be performed by that system.
Diagnostic Software Systems:
These systems helps in diagnosing the computer hardware components. When a new device is plugged into your computer, a diagnostic software system does some work. The "New Hardware Found" dialog can be seen as a result of this system.

Sensor and Signal Processing Systems:
The message processing system helps in sending and receiving messages. These systems are more complex because they make use of mathematics for signal processing. In a signal processing system, the computer receives input in the form of signals and then transforms the signals to a user understandable output.

Simulation Systems:
Simulation is the process of designing a model of a real system and conducting experiments with this model for the purpose of understanding the behavior of the system or evaluating various strategies for the operation of the system. A simulation is a software package that re-creates or simulates, albeit in a simplified manner, a complex phenomenon, environment, experience providing the user an opportunity for some new level of understanding.
Simulation systems are easier, cheaper and safer to use as compared to real systems and often the only way to build the real systems. For example, learning to fly a fighter plane using a simulator is much safer and less expensive than learning on a real fighter plane. System simulation mimics the operation of a real system such as the operation in a bank or the running of an assembly line in a factory.
Simulation in the early stage of design cycle is important because the cost of mistakes increases dramatically later in the product life cycle. Also, simulation software can analyze the operation of a real system without the improvement of an expert i.e. it can also be analyzed with a non-expert like a manager.