In recent years, the notion of local robustness (or robustness for short) has emerged as a
desirable property of deep neural networks. Intuitively, robustness means that small …

Understanding program behaviors is important to verify program properties or to optimize
programs. Static analysis is a widely used technique to approximate program behaviors via …

Consider a sequential programming language with control flow constructs such as
assignments, choice, loops, and procedure calls. We restrict the syntax of expressions in this …

We present a high-level survey of state-of-the-art alias analyses for object-oriented
programs, based on a years-long effort develo** industrial-strength static analyses for …

The delta debugging problem concerns how to reduce an object while preserving a certain
property, and widely exists in many applications, such as compiler development, regression …

We present a method for selectively applying context-sensitivity during interprocedural
program analysis. Our method applies context-sensitivity only when and where doing so is …

We present a new data-driven approach to achieve highly cost-effective context-sensitive
points-to analysis for Java. While context-sensitivity has greater impact on the analysis …

We present a machine-learning-based technique for selectively applying unsoundness in
static analysis. Existing bug-finding static analyzers are unsound in order to be precise and …

Building a cost-effective static analyser for real-world programs is still regarded an art. One
key contributor to this grim reputation is the difficulty in balancing the cost and the precision …

We present Graphick, a new technique for automatically learning graph-based heuristics for
pointer analysis. Striking a balance between precision and scalability of pointer analysis …