Symbolic Robustness Analysis
By Rupak Majumdar, Indranil Saha. 2009
56 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Software
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Timon Gehr
- Martin T. Vechev
- Mark Harman
- Edmund Kieran Burke
- Sumit Gulwani
- Myra B. Cohen
- Phil McMinn
- Saswat Anand
In Software
- Mark Harman
- Swarat Chaudhuri
- Phil McMinn
- Myra B. Cohen
- Koushik Sen
- Martin T. Vechev
- Sumit Gulwani
- Petar Tsankov
Citations in the corpus
- AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation (2018, methodology): “[28] considered a definition of robustness that is similar to the one in our work, and [5] used a combination of abstract interpretation and SMT-based methods to prove robustness of programs.”
- Robustness Analysis of Finite Precision Implementations (2013, unknown): “In [20], the algorithm proposed by the authors symbolically traverses program paths and collects constraints on input and output variables.”
- An orchestrated survey of methodologies for automated software test case generation (2013, background): “Majumdar and Saha (2009) use symbolic execution to generate test data whose slight perturbation causes a significant difference in a program's output.”
- Proving programs robust (2011, background): “[17] R. Majumdar, I. Saha, and Zilong Wang.”