Automated Repair of Stateflow Models for Cyber-Physical Systems Controllers

Concetti Chiave

A novel search-based approach called FLOWREPAIR that can automatically repair faults in Stateflow models, the de-facto language for modeling the high-level control logic of Cyber-Physical Systems.

Sintesi

The paper proposes FLOWREPAIR, a novel search-based approach for automatically repairing faults in Stateflow models, which are widely used to model the high-level control logic of Cyber-Physical Systems (CPSs) in Simulink.
The key highlights and insights are:

FLOWREPAIR combines a global search algorithm with a local search algorithm to efficiently generate patches for Stateflow models. The global search explores different kinds of patches across the model, while the local search exploits promising partial patches.

FLOWREPAIR defines three novel repair objectives tailored for CPSs, focusing on the time the failure is active, the time the failure is triggered, and the severity of the failure. These objectives are more suitable than the traditional pass/fail test case counts used in classical software APR.

FLOWREPAIR provides a set of 15 mutation operators specifically designed for repairing Stateflow models, covering operations like relational operator replacement, conditional operator replacement, and state/transition modifications.

The evaluation on 9 real faults across 3 CPS case studies shows that FLOWREPAIR can generate plausible patches for 8 out of the 9 faults, and valid patches for 6 out of the 9 faults. FLOWREPAIR also outperforms or performs similarly to a baseline APR technique.

The authors provide a replication package and a live repository, paving the way towards the APR of CPSs modeled in Simulink.

Statistiche

The time the failure is active is reduced compared to the initial faulty behavior.
The time the failure is triggered is postponed compared to the initial faulty behavior.
The severity of the failure is reduced compared to the initial faulty behavior.

Citazioni

"Stateflow models are widely used in the industry to model the high-level control logic of Cyber-Physical Systems (CPSs) in Simulink–the defacto CPS simulator."
"Many approaches exist to test Simulink models, but once a fault is detected, the process to repair it remains manual. Such a manual process increases the software development cost, making it paramount to develop novel techniques that reduce this cost."
"The novelty of FLOWREPAIR includes, (1) a new algorithm that combines global and local search for patch generation; (2) a definition of novel repair objectives (e.g., the time a fault remained active) specifically designed for repairing CPSs; and (3) a set of mutation operators to repair Stateflow models automatically."

Approfondimenti chiave tratti da

Search-based Automated Program Repair of CPS Controllers Modeled in Simulink-Stateflow

by Aitor Arriet... alle arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04688.pdf

Domande più approfondite

How can the proposed repair objectives be extended or combined to further improve the effectiveness of FLOWREPAIR?

The proposed repair objectives in FLOWREPAIR focus on the time a failure is active, the time the failure is triggered, and the severity of the failure. To further improve the effectiveness of FLOWREPAIR, these repair objectives can be extended or combined in the following ways:

Incorporating Resource Utilization: Include repair objectives related to resource utilization, such as memory usage or CPU load, especially in resource-constrained CPSs where efficient resource management is crucial.

Safety and Security Metrics: Integrate repair objectives that focus on safety and security metrics, ensuring that the repaired system meets specific safety and security requirements.

Fault Impact Analysis: Include repair objectives that analyze the impact of the fault on the overall system behavior, helping prioritize critical faults for repair.

Feedback Loop: Implement a feedback loop mechanism that dynamically adjusts repair objectives based on the effectiveness of previous repair attempts, allowing the system to learn and improve over time.

Multi-Objective Optimization: Combine repair objectives into a multi-objective optimization framework to balance conflicting objectives and find optimal solutions that satisfy multiple criteria simultaneously.

By extending and combining repair objectives in these ways, FLOWREPAIR can enhance its ability to generate effective patches for Stateflow models in CPS controllers.

How can the proposed approach be adapted to handle other CPS modeling languages beyond Stateflow, such as SysML or AADL?

To adapt the proposed approach to handle other CPS modeling languages beyond Stateflow, such as SysML or AADL, the following steps can be taken:

Language-Specific Mutation Operators: Develop language-specific mutation operators tailored to the syntax and semantics of the target modeling language. This involves understanding the unique features and constructs of SysML or AADL and designing mutations that are relevant to these languages.

Model Transformation: Implement model transformation techniques to convert models from SysML or AADL into a format compatible with the repair algorithm designed for Stateflow models. This may involve mapping elements between different modeling languages and ensuring consistency in the transformation process.

Domain-Specific Repair Objectives: Define repair objectives that are specific to the characteristics and requirements of SysML or AADL models. Consider factors such as timing constraints, communication protocols, and system architecture when formulating repair objectives for these languages.

Tool Integration: Integrate the repair approach with existing tools and frameworks used for SysML or AADL modeling and analysis. This integration ensures seamless interaction between the repair algorithm and the modeling environment, facilitating the repair process for CPS controllers modeled in these languages.

Validation and Verification: Conduct thorough validation and verification of the adapted approach on a diverse set of CPS models represented in SysML or AADL. This validation process helps ensure the effectiveness and applicability of the repair technique across different modeling languages.

By following these adaptation strategies, the proposed approach can be successfully extended to handle CPS modeling languages beyond Stateflow, enabling automated program repair for a wider range of CPS controllers.