33 citations · 47 across the 10 of their papers we have counts for
10 papers
A Fast Quantitative Analyzer for NetKAT
Thomas Lu, Qiancheng Fu, Kevin Batz +5
When designing a network, engineers must navigate trade-offs (e.g., one topology offers more aggregate bandwidth, another lower latency or better resilience) that demand reasoning…
SMT-Based Active Learning of Weighted Automata
Tiago Ferreira, Kevin Batz, Alexandra Silva
We present an SMT-based active learning algorithm for nondeterministic weighted automata (WFAs) as a practical and robust alternative to Hankel/L*-style methods. Our algorithm is p…
Weighted NetKAT: A Programming Language For Quantitative Network Verification
Emmanuel Suárez Acevedo, Tiago Ferreira, Kevin Batz +3
We introduce weighted NetKAT, a domain-specific language for modeling and verifying quantitative network properties. The language is parametric on a semiring, enabling the treatmen…
RFSeek and Ye Shall Find
Noga H. Rotman, Tiago Ferreira, Hila Peleg +2
Requests for Comments (RFCs) are extensive specification documents for network protocols, but their prose-based format and their considerable length often impede precise operationa…
Active Learning of Symbolic NetKAT Automata
Mark Moeller, Tiago Ferreira, Thomas Lu +2
NetKAT is a domain-specific programming language and logic that has been successfully used to specify and verify the behavior of packet-switched networks. This paper develops techn…
Conflict-Aware Active Automata Learning
Tiago Ferreira, Léo Henry, Raquel Fernandes da Silva +1
Active automata learning algorithms cannot easily handle conflict in the observation data (different outputs observed for the same inputs). This inherent inability to recover after…
Conflict-Aware Active Automata Learning (Extended Version)
Tiago Ferreira, Léo Henry, Raquel Fernandes da Silva +1
Active automata learning algorithms cannot easily handle conflict in the observation data (different outputs observed for the same inputs). This inherent inability to recover after…
Tree-Based Adaptive Model Learning
Tiago Ferreira, Gerco van Heerdt, Alexandra Silva
We extend the Kearns-Vazirani learning algorithm to be able to handle systems that change over time. We present a new learning algorithm that can reuse and update previously learne…
Prognosis: Closed-Box Analysis of Network Protocol Implementations
Tiago Ferreira, Harrison Brewton, Loris D'Antoni +1
We present Prognosis, a framework offering automated closed-box learning and analysis of models of network protocol implementations. Prognosis can learn models that vary in abstrac…
Symbolic Register Automata
Loris D'Antoni, Tiago Ferreira, Matteo Sammartino +1
Symbolic Finite Automata and Register Automata are two orthogonal extensions of finite automata motivated by real-world problems where data may have unbounded domains. These automa…