| Name | psy-taliro JSON |
| Version |
1.0.0
JSON |
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| home_page | None |
| Summary | Search-based test generation framework |
| upload_time | 2024-03-21 22:55:14 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
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No requirements were recorded.
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# $\Psi$-TaLiRo
An extensible Python toolbox for search-based test generation for cyber-physical
systems. This work is based of the MATLAB toolbox S-TaLiRo, which is available
[here](https://sites.google.com/a/asu.edu/s-taliro/s-taliro).
## Models
$\Psi$-TaLiRo represents the cyber-physical system under test as a model.
Conceptually, a model is a function that maps a set of system inputs to a
timed series of states called a _Trace_. $\Psi$-TaLiRo provides two ways to
construct a model: the __Blackbox__ and __Ode__ models. The __Blackbox__ model
makes no assumptions about the underlying system it represents. The __Ode__
model expects the underlying model to be represented using ordinary
differential equations.
## Specifications
$\Psi$-TaLiRo tests are system requirements expressed in metric temporal logic
(MTL). Evaluation of the system requirement depends on a _monitor_, and
$\Psi$-TaLiRo supports several options. All specific implementations are
available in the `staliro.specifications` module. To use a specification, you
will need to ensure that the monitor library is installed. Additional
information is available in the table below:
| Monitor | Installation | Link |
| ------------------ | ---------------------- | ------------------------------------------------ |
| RTAMT | `pip install rtamt` | [homepage](https://github.com/nickovic/rtamt) |
| TLTK (*Linux only) | `pip install tltk_mtl` | [homepage](https://bitbucket.org/versyslab/tltk) |
| Py-TaLiRo | `pip install pytaliro` | [homepage](https://gitlab.com/sbtg/py-taliro) |
## Optimizers
$\Psi$-TaLiRo generates inputs for the system under test by using an optimizer.
The optimizers provided by the toolbox are the __UniformRandom__ and
__DualAnnealing__ optimizers in the `staliro.optimizers` module.
For other optimizer options, see the [PartX](https://gitlab.com/bose1/part-x)
repository. There you will find additional implementations that give extra
guarantees about the system input space.
## Type hints
This toolbox provides [PEP484](https://peps.python.org/pep-0484) type hints
to help ensure correct usage. To use the type hints, you will need to install
one of the several type hint checkers available for python. A non-exhaustive
list is:
- [MyPy](https://mypy.readthedocs.io)
- [Pyright](https://github.com/microsoft/pyright)
- [Pyre](https://github.com/facebook/pyre-check)
- [Pytype](https://github.com/google/pytype)
The easiest way to get started is to install
[VSCode](https://code.visualstudio.com) with the
[python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
which includes the _pyright_ type checker.
## Installation
To install this toolbox, run the command `pip install psy_taliro`. To avoid
installing python packages globally, you can use a virtual environment to
keep the packages in a project-specific directory. Some of the tools for
managing virtual environments are:
- [Pipenv](https://pipenv.pypa.io)
- [Poetry](https://python-poetry.org)
- [Venv](https://docs.python.org/3/library/venv.html)
(Anaconda)[https://www.anaconda.com] can also be used to create separate python
environments, and may be easier to set up on some systems.
## Example
```python
from math import pi
from staliro import models, optimizers, specifications
from staliro.options import Options
from staliro.staliro import staliro
@models.blackbox()
def aircraft_model(X, T, U):
"""Blackbox model that represents the dynamics of an aircraft.
Arguments:
X: The static (initial) inputs to the system. A four-element vector of
the form [roll, pitch, yaw, thrust].
T: Interpolation times for the input signals
U: Interpolated values of the time-varying input signals
Returns:
trace: A set of timed state values representing the altitude of the
aircraft over time
"""
...
optimizer = optimizers.UniformRandom()
requirement = "[] (alt > 0.0)" # Requirement that the aircraft does not crash
specification = specifications.RTAMTDense(requirements, {"alt": 0}) # The altitude value is in the first column of the aircraft trace states
options = Options(
runs=10, # 10 independent optimization attempts
iterations=100, # Generate 100 samples per optimization attempt
interval=(0.0, 2.0), # Simulation interval is from 0 to 2 seconds
static_parameters=[
(-pi / 4, pi / 4), # Roll
(-pi / 4, pi / 4), # Pitch
(-pi / 4, pi / 4), # Yaw
(0, 100), # Thrust
]
)
result = staliro(aircraft_model, specification, optimizer, options)
```
## Documentation
For additional details about the toolbox components, or example scripts, refer
to the [documentation site](https://sbtg.gitlab.io/psy-taliro).
## Citing this project
If you use this toolbox in your research, include this citation in your
bibliography
```bibtex
@misc{psy-taliro,
doi = {10.48550/ARXIV.2106.02200},
url = {https://arxiv.org/abs/2106.02200},
author = {Thibeault, Quinn and Anderson, Jacob and Chandratre, Aniruddh and Pedrielli, Giulia and Fainekos, Georgios},
keywords = {Software Engineering (cs.SE), Systems and Control (eess.SY), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems},
publisher = {arXiv},
year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
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"description": "# $\\Psi$-TaLiRo\n\nAn extensible Python toolbox for search-based test generation for cyber-physical\nsystems. This work is based of the MATLAB toolbox S-TaLiRo, which is available\n[here](https://sites.google.com/a/asu.edu/s-taliro/s-taliro).\n\n## Models\n\n$\\Psi$-TaLiRo represents the cyber-physical system under test as a model.\nConceptually, a model is a function that maps a set of system inputs to a\ntimed series of states called a _Trace_. $\\Psi$-TaLiRo provides two ways to\nconstruct a model: the __Blackbox__ and __Ode__ models. The __Blackbox__ model\nmakes no assumptions about the underlying system it represents. The __Ode__\nmodel expects the underlying model to be represented using ordinary\ndifferential equations.\n\n## Specifications\n\n$\\Psi$-TaLiRo tests are system requirements expressed in metric temporal logic\n(MTL). Evaluation of the system requirement depends on a _monitor_, and\n$\\Psi$-TaLiRo supports several options. All specific implementations are\navailable in the `staliro.specifications` module. To use a specification, you\nwill need to ensure that the monitor library is installed. Additional\ninformation is available in the table below:\n\n| Monitor | Installation | Link |\n| ------------------ | ---------------------- | ------------------------------------------------ |\n| RTAMT | `pip install rtamt` | [homepage](https://github.com/nickovic/rtamt) |\n| TLTK (*Linux only) | `pip install tltk_mtl` | [homepage](https://bitbucket.org/versyslab/tltk) |\n| Py-TaLiRo | `pip install pytaliro` | [homepage](https://gitlab.com/sbtg/py-taliro) |\n\n## Optimizers\n\n$\\Psi$-TaLiRo generates inputs for the system under test by using an optimizer.\nThe optimizers provided by the toolbox are the __UniformRandom__ and\n__DualAnnealing__ optimizers in the `staliro.optimizers` module.\n\nFor other optimizer options, see the [PartX](https://gitlab.com/bose1/part-x)\nrepository. There you will find additional implementations that give extra\nguarantees about the system input space.\n\n## Type hints\n\nThis toolbox provides [PEP484](https://peps.python.org/pep-0484) type hints\nto help ensure correct usage. To use the type hints, you will need to install\none of the several type hint checkers available for python. A non-exhaustive\nlist is:\n\n- [MyPy](https://mypy.readthedocs.io)\n- [Pyright](https://github.com/microsoft/pyright)\n- [Pyre](https://github.com/facebook/pyre-check)\n- [Pytype](https://github.com/google/pytype)\n\nThe easiest way to get started is to install\n[VSCode](https://code.visualstudio.com) with the\n[python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)\nwhich includes the _pyright_ type checker.\n\n## Installation\n\nTo install this toolbox, run the command `pip install psy_taliro`. To avoid\ninstalling python packages globally, you can use a virtual environment to\nkeep the packages in a project-specific directory. Some of the tools for\nmanaging virtual environments are:\n\n- [Pipenv](https://pipenv.pypa.io)\n- [Poetry](https://python-poetry.org)\n- [Venv](https://docs.python.org/3/library/venv.html)\n\n(Anaconda)[https://www.anaconda.com] can also be used to create separate python\nenvironments, and may be easier to set up on some systems.\n\n## Example\n\n```python\nfrom math import pi\n\nfrom staliro import models, optimizers, specifications\nfrom staliro.options import Options\nfrom staliro.staliro import staliro\n\n\n@models.blackbox()\ndef aircraft_model(X, T, U):\n \"\"\"Blackbox model that represents the dynamics of an aircraft.\n\n Arguments:\n X: The static (initial) inputs to the system. A four-element vector of\n the form [roll, pitch, yaw, thrust].\n T: Interpolation times for the input signals\n U: Interpolated values of the time-varying input signals\n\n Returns:\n trace: A set of timed state values representing the altitude of the\n aircraft over time\n \"\"\"\n ...\n\n\noptimizer = optimizers.UniformRandom()\nrequirement = \"[] (alt > 0.0)\" # Requirement that the aircraft does not crash\nspecification = specifications.RTAMTDense(requirements, {\"alt\": 0}) # The altitude value is in the first column of the aircraft trace states\noptions = Options(\n runs=10, # 10 independent optimization attempts\n iterations=100, # Generate 100 samples per optimization attempt\n interval=(0.0, 2.0), # Simulation interval is from 0 to 2 seconds\n static_parameters=[\n (-pi / 4, pi / 4), # Roll\n (-pi / 4, pi / 4), # Pitch\n (-pi / 4, pi / 4), # Yaw\n (0, 100), # Thrust\n ]\n)\n\nresult = staliro(aircraft_model, specification, optimizer, options)\n```\n\n## Documentation\n\nFor additional details about the toolbox components, or example scripts, refer\nto the [documentation site](https://sbtg.gitlab.io/psy-taliro).\n\n## Citing this project\n\nIf you use this toolbox in your research, include this citation in your\nbibliography\n\n```bibtex\n@misc{psy-taliro,\n doi = {10.48550/ARXIV.2106.02200},\n url = {https://arxiv.org/abs/2106.02200},\n author = {Thibeault, Quinn and Anderson, Jacob and Chandratre, Aniruddh and Pedrielli, Giulia and Fainekos, Georgios},\n keywords = {Software Engineering (cs.SE), Systems and Control (eess.SY), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},\n title = {PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems},\n publisher = {arXiv},\n year = {2021},\n copyright = {arXiv.org perpetual, non-exclusive license}\n}\n```\n\n",
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