Dynamic Slicing by On-demand Re-execution

TL;DR

This paper introduces an on-demand re-execution method for dynamic slicing that shifts complexity from execution size to slice size, improving performance especially for small slices in large executions.

Contribution

It presents a novel on-demand re-execution algorithm utilizing frontier dependency and static analysis to efficiently construct dynamic slices.

Findings

Performance gains for small slices in large executions

Effective use of static analysis to focus dependency tracking

Evaluation on SV-COMP and unit tests supports approach's efficiency

Abstract

In this paper, we propose a novel approach that aims to offer an alternative to the prevalent paradigm to dynamic slicing construction. Dynamic slicing requires dynamic data and control dependencies that arise in an execution. During a single execution, memory reference information is recorded and then traversed to extract dependencies. Execute-once approaches and tools are challenged even by executions of moderate size of simple and short programs. We propose to shift practical time complexity from execution size to slice size. In particular, our approach executes the program multiple times while tracking targeted information at each execution. We present a concrete algorithm that follows an on-demand re-execution paradigm that uses a novel concept of frontier dependency to incrementally build a dynamic slice. To focus dependency tracking, the algorithm relies on static analysis. We show results of an evaluation on the SV-COMP benchmark and Antrl4 unit tests that provide evidence that on-demand re-execution can provide performance gains particularly when slice size is small and execution size is large.