# SlipCover: Near Zero-Overhead Code Coverage for Python

arXiv: https://arxiv.org/abs/2305.02886 · HTML: https://mlsanity.com/paper/2305.02886/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2023.

## Built on by (most cited first, 3 of 3)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [CoverUp: Effective High Coverage Test Generation for Python](https://mlsanity.com/paper/2403.16218/) (2024)
- [Intersectoral Knowledge in AI and Urban Studies: A Framework for Transdisciplinary Research](https://mlsanity.com/paper/2508.07507/) (2025)

## Abstract (arXiv)

Coverage analysis is widely used but can suffer from high overhead. This overhead is especially acute in the context of Python, which is already notoriously slow (a recent study observes a roughly 30x slowdown vs. native code). We find that the state-of-the-art coverage tool for Python, coverage.py, introduces a median overhead of 180% with the standard Python interpreter. Slowdowns are even more extreme when using PyPy, a JIT-compiled Python implementation, with coverage.py imposing a median overhead of 1,300%. This performance degradation reduces the utility of coverage analysis in most use cases, including testing and fuzzing, and precludes its use in deployment. This paper presents SlipCover, a novel, near-zero overhead coverage analyzer for Python. SlipCover works without modifications to either the Python interpreter or PyPy. It first processes a program's AST to accurately identify all branches and lines. SlipCover then dynamically rewrites Python bytecodes to add lightweight instrumentation to each identified branch and line. At run time, SlipCover periodically de-instruments already-covered lines and branches. The result is extremely low overheads -- a median of just 5% -- making SlipCover suitable for use in deployment. We show its efficiency can translate to significant increases in the speed of coverage-based clients. As a proof of concept, we integrate SlipCover into TPBT, a targeted property-based testing system, and observe a 22x speedup.