ATOS: Adaptive Program Tracing With Online Control Flow Graph Support

Abstract

Program tracing solutions (i.e., tracers) can faithfully record runtime information about a program's execution and enable flexible and powerful offline analysis. Therefore, they have become fundamental techniques extensively utilized in software analysis applications. However, few tracers have paid attention to the size of traces and corresponding overheads introduced to offline analysis, as well as the Control Flow Graph (CFG) support. This paper presents ATOS, an efficient tracing solution, to address these issues. It adaptively adjusts the granularity of tracing while conservatively preserving the essential execution information. We implement a prototype of ATOS and evaluate it on several benchmarks. The results show that ATOS can greatly reduce the size of a trace and accelerate offline analysis, while preserving the execution states and supporting existing applications seamlessly. For example, using ATOS, the trace produced by the application CryptoHunt is reduced by 46 times, while the analysis time is reduced by 34 times.

Keywords

• Program tracing
• control flow graph
• loop optimization
• adaptive granularity
• check point