Multicore technology has moved concurrent programming to the forefront of computer science. In this thesis, we look at the problem of reasoning about concurrent systems with infinite data domains and non-deterministic input, and develop a method for verification and falsification of safety properties of such systems. Novel characteristics of this method matching and (b) proving safety using under-approximating models. We compare our approach with recent related work from literature and show that our approach is generally superior both at verification and falsification.

Self-composition is a logical formulation of non-interference, a high-level security property that guarantees the absence of illicit information leakages through executing programs. In order to capture program executions, self-composition has been expressed in Hoare or modal logic, and has been

Abstract. In this paper we propose a method for inferring invariants for loops in Java programs. An example of a simple while loop is used throughout the paper to explain our approach. The method is based on a combination of symbolic execution and computing fixed points via predicate abstraction

We present a new approach to automated reasoning about higher-order programs by extending symbolic execution to use behavioral contracts as symbolic values, thus enabling symbolic approximation of higher-order behavior. Our approach is based on the idea of an abstract reduction semantics that gives

properties of such systems. Novel characteristics of this method are (a) constructing under-approximating models via symbolic execution with abstract matching and (b) proving safety using underapproximating models.

Interleaving planning and execution is the practical alternative to the problem of planning off-line with large state spaces. While planning via symbolic model checking has been extensively studied for off-line planning, no framework for interleaving it with execution has been ever devised

generated using our framework can achieve higher branch coverage than the ones generated by evolutionary testing, symbolic execution, or random testing within the same amount of time. Improving Structural Testing of Object-Oriented Programs via Integrating

Symbolic execution is an automated technique for program testing that has recently become practical, thanks to advances in constraint solvers. Generally speaking, a symbolic executor interprets a program with symbolic inputs, systematically enumerating execution paths induced by the symbolic inputs

highprobability and low-probability paths through probabilistic symbolic execution. Once a path is explored, it generates and runs a set of test inputs to model the performance of the path. Finally, it constructs the performance distribution for the program. We have implemented PerfPlotter based on the Symbolic

structure and semantics. Second, covering some branches requires special method sequences to lead the receiver object or non-primitive arguments to specific desirable states. Previous work has developed the symbolic execution technique and the evolutionary testing technique to address these two challenges