A Generic and Efficient Python Runtime Verification System and its Large-scale Evaluation

• URL: http://arxiv.org/abs/2509.06324v1
• Date: Mon, 08 Sep 2025 04:11:28 GMT
• Title: A Generic and Efficient Python Runtime Verification System and its Large-scale Evaluation
• Abstract summary: We propose PyMOP, a generic, efficient RV system for Python.<n>PyMOP supports five logics, five existing monitoring algorithms, ships with 73 API specs of Python and widely-used libraries, and users can easily add more.<n>On 290,133 unit tests in 1,463 GitHub projects, we find mainly that (i) the default monitoring algorithm for Java is often not the fastest for Python; (ii) PyMOP is up to 1,168.3x faster than two recent dynamic analysis systems; and (iii) 44 of 121 bugs that PyMOP helped find so far were fixed by developers
• Score: 12.963005661353476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Runtime verification (RV) now scales for testing thousands of open-source Java projects, helping find hundreds of bugs. The popular Python ecosystem could use such benefits. But, today's Python RV systems are limited to a domain or specification logic, or slow. We propose PyMOP, a generic, extensible, and efficient RV system for Python. PyMOP supports five logics, implements five existing monitoring algorithms, ships with 73 API specs of Python and widely-used libraries, supports three instrumentation strategies, and users can easily add more of these. On 290,133 unit tests in 1,463 GitHub projects, we find mainly that (i) the default monitoring algorithm for Java is often not the fastest for Python; (ii) PyMOP is up to 1,168.3x faster than two recent dynamic analysis systems; and (iii) 44 of 121 bugs that PyMOP helped find so far were fixed by developers. PyMOP's generality and efficiency position it well as an excellent platform for the next advances on RV for Python.

Related papers

• pyGinkgo: A Sparse Linear Algebra Operator Framework for Python [0.0]
  pyGinkgo is a lightweight and Pythonic interface to the Ginkgo library.<n> pyGinkgo bridges the gap between high-performance C++ backends and Python by exposing Ginkgo's capabilities via Pybind11 and aPy and PyTorch compatible interface.
  arXiv  Detail & Related papers  (2025-10-09T13:55:51Z)
• Dynamic and Static Analysis of Python Software with Kieker Including Reconstructed Architectures [0.2867517731896504]
  The Kieker observability framework is a tool that provides users with the means to design a custom observability pipeline for their application.<n>Originally tailored for Java, supporting Python with Kieker is worthwhile.
  arXiv  Detail & Related papers  (2025-07-31T11:05:23Z)
• EnvBench: A Benchmark for Automated Environment Setup [76.02998475135824]
  Large Language Models have enabled researchers to focus on practical repository-level tasks in software engineering domain.<n>Existing studies on environment setup introduce innovative agentic strategies, but their evaluation is often based on small datasets.<n>To address this gap, we introduce a comprehensive environment setup benchmark EnvBench.
  arXiv  Detail & Related papers  (2025-03-18T17:19:12Z)
• PyPulse: A Python Library for Biosignal Imputation [58.35269251730328]
  We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings.<n>PyPulse's framework provides a modular and extendable framework with high ease-of-use for a broad userbase, including non-machine-learning bioresearchers.<n>We released PyPulse under the MIT License on Github and PyPI.
  arXiv  Detail & Related papers  (2024-12-09T11:00:55Z)
• PyGOD: A Python Library for Graph Outlier Detection [56.33769221859135]
  PyGOD is an open-source library for detecting outliers in graph data. It supports a wide array of leading graph-based methods for outlier detection. PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI)
  arXiv  Detail & Related papers  (2022-04-26T06:15:21Z)
• PyHHMM: A Python Library for Heterogeneous Hidden Markov Models [63.01207205641885]
  PyHHMM is an object-oriented Python implementation of Heterogeneous-Hidden Markov Models (HHMMs) PyHHMM emphasizes features not supported in similar available frameworks: a heterogeneous observation model, missing data inference, different model order selection criterias, and semi-supervised training. PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages, and is distributed under the Apache-2.0 License.
  arXiv  Detail & Related papers  (2022-01-12T07:32:36Z)
• OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems [1.066106854070245]
  OMB-Py is the first communication benchmark suite for parallel Python applications. OMB-Py consists of a variety of point-to-point and collective communication benchmark tests. We report up to 106x speedup on 224 CPU cores compared to sequential execution.
  arXiv  Detail & Related papers  (2021-10-20T16:59:14Z)
• Extending Python for Quantum-Classical Computing via Quantum Just-in-Time Compilation [78.8942067357231]
  Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. We present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time compilation.
  arXiv  Detail & Related papers  (2021-05-10T21:11:21Z)
• Python Workflows on HPC Systems [2.1485350418225244]
  The recent successes and wide spread application of compute intensive machine learning and data analytics methods have been boosting the usage of the Python programming language on HPC systems. While Python provides many advantages for the users, it has not been designed with a focus on multi-user environments or parallel programming. In this paper, we analyze the key problems induced by the usage of Python on HPC clusters and sketch appropriate workarounds.
  arXiv  Detail & Related papers  (2020-12-01T09:51:12Z)
• PySAD: A Streaming Anomaly Detection Framework in Python [0.0]
  Streaming anomaly detection requires algorithms that operate under strict constraints.<n>We present PySAD, a comprehensive Python framework addressing these challenges through a unified architecture.
  arXiv  Detail & Related papers  (2020-09-05T17:41:37Z)
• OPFython: A Python-Inspired Optimum-Path Forest Classifier [68.8204255655161]
  This paper proposes a Python-based Optimum-Path Forest framework, denoted as OPFython. As OPFython is a Python-based library, it provides a more friendly environment and a faster prototyping workspace than the C language.
  arXiv  Detail & Related papers  (2020-01-28T15:46:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.