17 citations · 25 across the 11 of their papers we have counts for
6 papers · 1 filter
Engineering an Efficient Boolean Functional Synthesis Engine
Priyanka Golia, Friedrich Slivovsky, Subhajit Roy +1
Given a Boolean specification between a set of inputs and outputs, the problem of Boolean functional synthesis is to synthesise each output as a function of inputs such that the sp…
Data-Driven Invariant Learning for Probabilistic Programs
Jialu Bao, Nitesh Trivedi, Drashti Pathak +2
Morgan and McIver's weakest pre-expectation framework is one of the most well-established methods for deductive verification of probabilistic programs. Roughly, the idea is to gene…
Program Synthesis as Dependency Quantified Formula Modulo Theory
Priyanka Golia, Subhajit Roy, Kuldeep S. Meel
Given a specification over inputs and output , defined over a background theory , the problem of program synthesis is to design a program such that…
Synthesizing Abstract Transformers
Pankaj Kumar Kalita, Sujit Kumar Muduli, Loris D'Antoni +2
This paper addresses the problem of creating abstract transformers automatically. The method we present automates the construction of static analyzers in a fashion similar to the w…
Input Validation with Symbolic Execution
Anay Mehrotra, Ayush Bansal, Awanish Pandey +1
Symbolic execution has always been plagued by the inability to handle programs that require highly structured inputs. Most often, the symbolic execution engine gets overwhelmed by…
Learning Differentially Private Mechanisms
Subhajit Roy, Justin Hsu, Aws Albarghouthi
Differential privacy is a formal, mathematical definition of data privacy that has gained traction in academia, industry, and government. The task of correctly constructing differe…