# Engineering Record And Replay For Deployability: Extended Technical Report

arXiv: https://arxiv.org/abs/1705.05937 · HTML: https://mlsanity.com/paper/1705.05937/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 5 of 5)

- [Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures](https://mlsanity.com/paper/2606.08275/) (2026)
- [ChatDBG: Augmenting Debugging with Large Language Models](https://mlsanity.com/paper/2403.16354/) (2024)
- [Resume Means Resume: A Machine-Checked Conformance Contract for Checkpoint, Interrupt, and Resume Semantics in Workflow Persistence Layers](https://mlsanity.com/paper/2608.03836/) (2026)
- [AUDITA: certified auditing and causal attribution of adverse outcomes in autonomous multi-agent systems](https://mlsanity.com/paper/2608.22160/) (2026)
- [DynBranch: Speculative Subgraph Reuse for Dynamic Agentic LLM Serving](https://mlsanity.com/paper/2609.31047/) (2026)

## Abstract (arXiv)

The ability to record and replay program executions with low overhead enables many applications, such as reverse-execution debugging, debugging of hard-to-reproduce test failures, and "black box" forensic analysis of failures in deployed systems. Existing record-and-replay approaches limit deployability by recording an entire virtual machine (heavyweight), modifying the OS kernel (adding deployment and maintenance costs), requiring pervasive code instrumentation (imposing significant performance and complexity overhead), or modifying compilers and runtime systems (limiting generality). We investigated whether it is possible to build a practical record-and-replay system avoiding all these issues. The answer turns out to be yes - if the CPU and operating system meet certain non-obvious constraints. Fortunately modern Intel CPUs, Linux kernels and user-space frameworks do meet these constraints, although this has only become true recently. With some novel optimizations, our system 'rr' records and replays real-world low-parallelism workloads with low overhead, with an entirely user-space implementation, using stock hardware, compilers, runtimes and operating systems. "rr" forms the basis of an open-source reverse-execution debugger seeing significant use in practice. We present the design and implementation of 'rr', describe its performance on a variety of workloads, and identify constraints on hardware and operating system design required to support our approach.