FREPA: An Automated and Formal Approach to Requirement Modeling and Analysis in Aircraft Control Domain
June 02, 2023 ยท Declared Dead ยท ๐ ESEC/SIGSOFT FSE
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jincao Feng, Weikai Miao, Hanyue Zheng, Yihao Huang, Jianwen Li, Zheng Wang, Ting Su, Bin Gu, Geguang Pu, Mengfei Yang, Jifeng He
arXiv ID
2306.01260
Category
cs.SE: Software Engineering
Citations
11
Venue
ESEC/SIGSOFT FSE
Last Checked
3 months ago
Abstract
Formal methods are promising for modeling and analyzing system requirements. However, applying formal methods to large-scale industrial projects is a remaining challenge. The industrial engineers are suffering from the lack of automated engineering methodologies to effectively conduct precise requirement models, and rigorously validate and verify (V&V) the generated models. To tackle this challenge, in this paper, we present a systematic engineering approach, named Formal Requirement Engineering Platform in Aircraft (FREPA), for formal requirement modeling and V\&V in the aerospace and aviation control domains. FREPA is an outcome of the seamless collaboration between the academy and industry over the last eight years. The main contributions of this paper include 1) an automated and systematic engineering approach FREPA to construct requirement models, validate and verify systems in the aerospace and aviation control domain, 2) a domain-specific modeling language AASRDL to describe the formal specification, and 3) a practical FREPA-based tool AeroReq which has been used by our industry partners. We have successfully adopted FREPA to seven real aerospace gesture control and two aviation engine control systems. The experimental results show that FREPA and the corresponding tool AeroReq significantly facilitate formal modeling and V&V in the industry. Moreover, we also discuss the experiences and lessons gained from using FREPA in aerospace and aviation projects.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Software Engineering
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
GraphCodeBERT: Pre-training Code Representations with Data Flow
R.I.P.
๐ป
Ghosted
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
R.I.P.
๐ป
Ghosted
Microservices: yesterday, today, and tomorrow
R.I.P.
๐ป
Ghosted
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
R.I.P.
๐ป
Ghosted
A Survey of Machine Learning for Big Code and Naturalness
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted