SPEAR is a simple Python tool that permits estimating the distance between two systems.
Each system consists of three distinct components:
- a process describing the behaviour of the program;
- a data space;
- an environment evolution describing the effect of the environment on the data space.
Two systems are compared via a (pseudo)metric, called the evolution metric. Thanks to the possibility of extrapolating process behaviour from that of the system typical of our model, this metric allows us to
- verify how well a program is fulfilling its tasks by comparing it with its specification;
- compare the activity of different programs in the same environment;
- compare the behaviour of one program with respect to different environments and changes in the initial environmental conditions.
Via the metric we introduce some dependability properties of a program, called robustness, adaptability, and reliability.
Robustness is the ability of the program to tolerate changes in the environmental conditions while preserving the original behaviour. Adaptability denotes the ability of the program to lead the system back to its desired behaviour, within a given time horizon, when some perturbation is applied to the initial data space. Reliability is the untimed version of adaptability.
In temperature.py we show how SPEAR can be used to model a simple heating system in which the thermostat, the program, has to keep the temperature of the room within a desired comfort interval. We use our algorithm to evaluate the differences between two heating systems having the same program but starting from different initial conditions. Finally, we apply it to study the adaptability and reliability of the considered program
In smart_room.py SPEAR is used to study adaptation of a domotic scenario where two parameters, temperature and air quality, are considered.
In redblue.py we use SPEAR to study adaptation of a Collective Adaptive Sysems by using mean-field approximation.
In LMCS_example_tanks.py we show how SPEAR can be used to model a stochastic variant of the 3-tanks laboratory experiment. Briefly, there are three identical tanks connected by two pipes. Water enters in the first and in the last tank by means of a pump and an incoming pipe, respectively. The last tank is equipped with an outlet pump. We assume that water flows through the incoming pipe with a rate that is determined by the environment, whereas the flow rates through the two pumps are under the control of the program controlling the experiment. The task of the program consists in guaranteeing that the levels of water in the three tanks fulfil some given requirements. We use our algorithms to evaluate the differences between various instances of this system, and to measure its robustness and its adaptability. Related to this example, in LMCS_statistical_error.py we provide a simple algorithm for the evaluation of the statistical error in the simulation of the evolution sequences of the three-tanks experiment.
In engine_system.py we present an application of the SPEAR tool to the analysis of an engine system. This consists of two supervised self-coordinating refrigerated engine systems that are subject to cyber-physical attacks aiming to inflict overstress of equipment.
To download SPEAR you have just to clone GitHub project:
git clone https://github.com/quasylab/spear.git
Run this command in the folder where you want to download the tool.
To run experiments Python3 (>= 3.9) is needed. Moreover, the following Python packages must be available:
- numpy >= 1.18.4
- scipy >= 1.4.1
- matplotlib >= 3.0.3
To install all the required packages you can just run:
pip install -r requirements.txt
If all the needed package are installed in your system, you have to execute:
python temperature.py
python smart_room.py
python redblue.py