Precise Null Pointer Analysis Through Global Value Numbering

Precise analysis of pointer information plays an important role in many static analysis techniques and tools today. The precision, however, must be balanced against the scalability of the analysis. This paper focusses on improving the precision of standard context and flow insensitive alias analysis algorithms at a low scalability cost. In particular, we present a semantics-preserving program transformation that drastically improves the precision of existing analyses when deciding if a pointer can alias NULL. Our program transformation is based on Global Value Numbering, a scheme inspired from compiler optimizations literature. It allows even a flow-insensitive analysis to make use of branch conditions such as checking if a pointer is NULL and gain precision. We perform experiments on real-world code to measure the overhead in performing the transformation and the improvement in the precision of the analysis. We show that the precision improves from 86.56% to 98.05%, while the overhead is insignificant.Comment: 17 pages, 1 section in Appendi

Static Deadlock Detection for Rust Programs

Rust relies on its unique ownership mechanism to ensure thread and memory safety. However, numerous potential security vulnerabilities persist in practical applications. New language features in Rust pose new challenges for vulnerability detection. This paper proposes a static deadlock detection method tailored for Rust programs, aiming to identify various deadlock types, including double lock, conflict lock, and deadlock associated with conditional variables. With due consideration for Rust's ownership and lifetimes, we first complete the pointer analysis. Then, based on the obtained points-to information, we analyze dependencies among variables to identify potential deadlocks. We develop a tool and conduct experiments based on the proposed method. The experimental results demonstrate that our method outperforms existing deadlock detection methods in precision

An incremental points-to analysis with CFL-reachability

Abstract. Developing scalable and precise points-to analyses is increasingly important for analysing and optimising object-oriented programs where pointers are used pervasively. An incremental analysis for a program updates the existing analysis information after program changes to avoid reanalysing it from scratch. This can be efficiently deployed in software development environments where code changes are often small and frequent. This paper presents an incremental approach for demand-driven context-sensitive points-to analyses based on Context-Free Language (CFL) reachability. By tracing the CFL-reachable paths traversed in computing points-to sets, we can precisely identify and recompute on demand only the points-to sets affected by the program changes made. Combined with a flexible policy for controlling the granularity of traces, our analysis achieves significant speedups with little space overhead over reanalysis from scratch when evaluated with a null dereferencing client using 14 Java benchmarks.