Efficient Interprocedural Analysis Research Articles

SafeDrop: Detecting Memory Deallocation Bugs of Rust Programs via Static Data-flow Analysis

Rust is an emerging programming language that aims to prevent memory-safety bugs. However, the current design of Rust also brings side effects, which may increase the risk of memory-safety issues. In particular, it employs ownership-based resource management and enforces automatic deallocation of unused resources without using the garbage collector. It may therefore falsely deallocate reclaimed memory and lead to use-after-free or double-free issues. In this article, we study the problem of invalid memory deallocation and propose SafeDrop , a static path-sensitive data-flow analysis approach to detect such bugs. Our approach analyzes each function of a Rust crate iteratively in a flow-sensitive and field-sensitive way. It leverages a modified Tarjan algorithm to achieve scalable path-sensitive analysis and a cache-based strategy for efficient inter-procedural analysis. We have implemented our approach and integrated it into the Rust compiler. Experiment results show that the approach can successfully detect all such bugs in our experiments with a limited number of false positives and incurs a very small overhead compared to the original compilation time.

Efficient interprocedural analysis for program parallelization and restructuring

An approach to efficient interprocedural analysis for program parallelization and restructuring is presented. Such analysis is needed for parallelizing loops which contain procedure calls. Our approach captures call effect on data dependencies by propagating the precise information of array subscripts from the called procedure. This allows the optimizing compiler to choose an efficient yet precise data dependence test scheme depending on the complexity of array reference patterns. The other existing methods do not provide such flexibility, hence may suffer from either imprecision or inefficiency. The paper also discusses usage of classical summary information in several important transformations for program parallelization. Experimental results are reported.