The tool analyzes BPMN4FRSS models representing forensic-ready software systems. The analysis process utilizes Z3 SMT Solver to evaluate the extent to which a system complies with forensic readiness requirements. Furthermore, it assesses system's ability to provide evidence with high evidentiary value in case of an incident. As a result, the implemented tool allows software engineers to inspect the possible model insufficiencies and offers hints to remedy the issue.

Forensic readiness requirements are captured in the form of rules. The rules are divided into three categories depending on the type of analysis they perform:

Semantic Rules Analysis

• verifies if the provided model is in accordance with BPMN4FRSS semantic rules. If the model violates any of the rules, the tool returns Errors that indicate the concrete problem and can help users fix the issue.

Semantic Hints Analysis

• provides recommendations for improving the model. Violating these rules results in Warnings but does not affect the model's validity. Nevertheless, compliance with these rules is highly recommended, as their violation can indicate potential weaknesses.

Evidence Quality Analysis

• checks whether the employed forensic-ready controls are sufficient to yield reliable and relevant potential evidence in case of an incident. To simulate an incident, the user can mark a concrete Data Store or a Flow Object (Task or Event) from the model as compromised, i.e., provide the id of a specific element within the analysis input.
  In both cases, analysis output is a list of Data Store elements that contain potential evidence. Stored potential evidence can hold information to help detect the inconsistency between incident related data.

Analysis input requires model path to the BPMN4FRSS model, command representing the analysis type and in case of Evidence Quality Analysis, the compromised element id as an argument.

To run the analysis, run one of the following commands in the root directory of the project:

• python main.py [OPTIONS] MODEL_PATH COMMAND [ARGS]
• poetry run main.py [OPTIONS] MODEL_PATH COMMAND [ARGS]

Use --help for more details.

To run all tests, run one of the following commands in the root directory of the project:

• pytest
• poetry run pytest

or to run just unit or integration tests:

• pytest tests/test_unit
• pytest tests/test_integration

To run the analysis exposed as REST API, run the following command:

• poetry run uvicorn src.api.api:app --reload

To install the dependencies:

• poetry install

MIT