[Paper Review] DoubleTake: Fast and Precise Error Detection via Evidence-Based Dynamic Analysis

Tongping Liu, Charlie Curtsinger+1 more|arXiv (Cornell University)|Jan 29, 2016

Parallel Computing and Optimization Techniques30 references4 citations

TL;DR

DoubleTake introduces evidence-based dynamic analysis, a technique that enables fast, precise, and low-overhead detection of memory errors in C/C++ programs by deferring instrumentation until error evidence is found. By checkpointing execution at epoch boundaries and re-running with instrumentation only when needed, DoubleTake achieves under 5% average performance overhead while precisely locating bugs like buffer overflows, use-after-free, and memory leaks.

ABSTRACT

This paper presents evidence-based dynamic analysis, an approach that enables lightweight analyses--under 5% overhead for these bugs--making it practical for the first time to perform these analyses in deployed settings. The key insight of evidence-based dynamic analysis is that for a class of errors, it is possible to ensure that evidence that they happened at some point in the past remains for later detection. Evidence-based dynamic analysis allows execution to proceed at nearly full speed until the end of an epoch (e.g., a heavyweight system call). It then examines program state to check for evidence that an error occurred at some time during that epoch. If so, it rolls back execution and re-executes the code with instrumentation activated to pinpoint the error. We present DoubleTake, a prototype evidence-based dynamic analysis framework. DoubleTake is practical and easy to deploy, requiring neither custom hardware, compiler, nor operating system support. We demonstrate DoubleTake's generality and efficiency by building dynamic analyses that find buffer overflows, memory use-after-free errors, and memory leaks. Our evaluation shows that DoubleTake is efficient, imposing just 4% overhead on average, making it the fastest such system to date. It is also precise: DoubleTake pinpoints the location of these errors to the exact line and memory addresses where they occur, providing valuable debugging information to programmers.

Motivation & Objective

• Address the high performance overhead of traditional dynamic analysis tools like Valgrind and AddressSanitizer, which hinder deployment in production environments.
• Overcome the limitation of existing tools that cannot detect errors in third-party libraries without recompilation or instrumentation.
• Enable precise, line-level error localization for memory errors such as buffer overflows, use-after-free, and memory leaks in deployed applications.
• Develop a practical, drop-in solution that requires no custom hardware, compiler, or OS support, making it easy to deploy in real-world systems.

Proposed method

• Use checkpointing at epoch boundaries (e.g., system calls) to capture program state, enabling rollback and re-execution with instrumentation only when needed.
• Leverage the monotonicity property of certain memory errors—where evidence of an error persists or grows over time—to detect past errors without continuous instrumentation.
• Plant 'tripwires' (e.g., canaries) in memory regions to ensure detectable evidence exists even if errors do not naturally leave traces.
• Interpose on system calls and library functions during normal execution to monitor for error indicators without performance cost.
• Upon detecting evidence of an error, roll back to the last checkpoint and re-execute with active instrumentation (e.g., hardware watchpoints) to pinpoint the exact line and memory address of the fault.
• Implement the framework as a drop-in shared library, requiring no recompilation of the target application or modifications to the OS or hardware.

Experimental results

Research questions

• RQ1 Can dynamic analysis be made efficient enough for deployment in production systems without sacrificing precision?
• RQ2 Can error evidence be reliably detected post hoc using lightweight monitoring, avoiding continuous instrumentation?
• RQ3 Can the performance overhead of dynamic analysis be reduced below 5% while still detecting critical memory errors?
• RQ4 Is it feasible to achieve precise error localization (to the exact line and memory address) using a rollback-and-replay mechanism with minimal runtime cost?
• RQ5 Can a system be built that works with unmodified binaries and third-party libraries without requiring recompilation or custom hardware?

Key findings

• DoubleTake achieves an average performance overhead of just 4% across the SPEC CPU2006 benchmark suite, making it the fastest dynamic analysis system to date.
• DoubleTake detects buffer overflows, use-after-free errors, and memory leaks with no false positives and provides precise error localization down to the exact line of code and memory address.
• The system imposes significantly lower overhead than Valgrind (which degrades performance by 17× on average) and AddressSanitizer (which incurs ~30% overhead).
• DoubleTake enables detection of memory leaks during program execution, not just at termination, making it suitable for long-lived applications like servers.
• The framework is deployable as a drop-in library with no need for recompilation, custom hardware, or OS modifications.
• DoubleTake outperforms related systems like Aftersight and Flashback in both overhead and deployment simplicity, avoiding reliance on virtual machines or additional processors.