# DyPyBench: A Benchmark of Executable Python Software

arXiv: https://arxiv.org/abs/2403.00539 · HTML: https://mlsanity.com/paper/2403.00539/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2024.

## Built on by (most cited first, 7 of 7)

- [You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects](https://mlsanity.com/paper/2412.10133/) (2024)
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- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [Morescient GAI for Software Engineering (Extended Version)](https://mlsanity.com/paper/2406.04710/) (2024)
- [CoCo-Bench: A Comprehensive Code Benchmark For Multi-task Large Language Model Evaluation](https://mlsanity.com/paper/2504.20673/) (2025)
- [Effective and Efficient Context Retrieval via Partial Dependency Graph for Repository-Level Code Generation](https://mlsanity.com/paper/2608.01927/) (2026)

## Abstract (arXiv)

Python has emerged as one of the most popular programming languages, extensively utilized in domains such as machine learning, data analysis, and web applications. Python's dynamic nature and extensive usage make it an attractive candidate for dynamic program analysis. However, unlike for other popular languages, there currently is no comprehensive benchmark suite of executable Python projects, which hinders the development of dynamic analyses. This work addresses this gap by presenting DyPyBench, the first benchmark of Python projects that is large scale, diverse, ready to run (i.e., with fully configured and prepared test suites), and ready to analyze (by integrating with the DynaPyt dynamic analysis framework). The benchmark encompasses 50 popular opensource projects from various application domains, with a total of 681k lines of Python code, and 30k test cases. DyPyBench enables various applications in testing and dynamic analysis, of which we explore three in this work: (i) Gathering dynamic call graphs and empirically comparing them to statically computed call graphs, which exposes and quantifies limitations of existing call graph construction techniques for Python. (ii) Using DyPyBench to build a training data set for LExecutor, a neural model that learns to predict values that otherwise would be missing at runtime. (iii) Using dynamically gathered execution traces to mine API usage specifications, which establishes a baseline for future work on specification mining for Python. We envision DyPyBench to provide a basis for other dynamic analyses and for studying the runtime behavior of Python code.