Fundamentals of Software Engineering (eBook, PDF)

10th International Conference, FSEN 2023, Tehran, Iran, May 4-5, 2023, Revised Selected Papers
Redaktion: Hojjat, Hossein; Ábrahám, Erika

• Format: PDF

Produktbeschreibung

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Fundamentals of Software Engineering, FSEN 2023, held Tehran, Iran in May 4-5, 2023.

The 9 full papers and 2 short papers presented in this volume were carefully reviewed and selected from 19 submissions. The topics of interest in FSEN span over all aspects of formal methods, especially those related to advancing the application of formal methods in the software industry and promoting their integration with practical engineering techniques. The papers are organized in topical sections on coordination, logic, networks, parallel computation, and testing.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 157
• Erscheinungstermin: 29. August 2023
• Englisch
• ISBN-13: 9783031424410
• Artikelnr.: 68781953

Inhaltsangabe

Structured specification of paraconsistent transition systems.- Towards a Basic Theory for Partial Differentiation in the Prototype Verification System.- Case studies of development of verified programs with Dafny for accessibility assessment.- TPGen: A Self-Stabilizing GPU-Based Method for Test and Prime Paths Generation.- An Optimised Complete Strategy for Testing Symbolic Finite State Machines.- Afra: An Eclipse-Based Tool with Extensible Architecture for Modeling and Model Checking of Rebeca Family Models.- Interaction-based Offline Runtime Verification of Distributed Systems.- Genetic Algorithm for Program Synthesis.- Property-Based Conjecturing for Automated Induction in Isabelle/HOL.- Verification of the busy-forbidden protocol.- kProp: Multi-Neuron Relaxation Method for Neural Network Robustness Verification.