Verifying Correctness of PLC Software during System Evolution using Model Containment Approach
September 06, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Soumyadip Bandyopadhyay, Santonu Sarkar
arXiv ID
2509.05596
Category
cs.SE: Software Engineering
Cross-listed
cs.SC
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Upgradation of Programmable Logic Controller (PLC) software is quite common to accommodate evolving industrial requirements. Verifying the correctness of such upgrades remains a significant challenge. In this paper, we propose a verification-based approach to ensure the correctness of the existing functionality in the upgraded version of a PLC software. The method converts the older and the newer versions of the sequential function chart (SFC) into two Petri net models. We then verify whether one model is contained within another, based on a novel containment checking algorithm grounded in symbolic path equivalence. For this purpose, we have developed a home-grown Petri net-based containment checker. Experimental evaluation on 80 real-world benchmarks from the OSCAT library highlights the scalability and effectiveness of the framework. We have compared our approach with verifAPS, a popular tool used for software upgradation, and observed nearly 4x performance improvement.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted