SpaceTime Programming: Live and Omniscient Exploration of Code and Execution

• URL: http://arxiv.org/abs/2603.18735v1
• Date: Thu, 19 Mar 2026 10:35:55 GMT
• Title: SpaceTime Programming: Live and Omniscient Exploration of Code and Execution
• Abstract summary: This paper introduces spacetime Programming, a programming model for exploring both code modifications and execution flow.<n>At the core of our approach is a trace mechanism that captures not only execution state but also the corresponding code changes.<n>As a proof of concept, we implemented a Python library supporting SpaceTime Programming and applied it in two contexts.
• Score: 2.914725395758538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Programming environments typically separate the world of static code from the dynamic execution of programs. Developers must switch between writing code and observing its execution, often with limited tools to understand the relationship between code changes and runtime behavior. Several paradigms and approaches exist to bridge this gap, including exploratory programming for comparing code variants, live programming for immediate feedback, and omniscient debugging for exploring execution history. However, existing solutions tend to focus on specific aspects and one specific paradigm rather than providing a fully integrated environment with multiple capabilities. This paper introduces \spacetime Programming, a novel approach that unifies these paradigms to create a programming model for exploring both code modifications and execution flow. At the core of our approach is a trace mechanism that captures not only execution state but also the corresponding code changes, enabling developers to explore programs in both space (code variants) and time (execution flow). As a proof of concept, we implemented a Python library supporting SpaceTime Programming and applied it in two contexts: a live omniscient debugger and a Pygame game development tool, showcased through a Flappy Bird-like game. We further evaluated SpaceTimePy on five real-world Python projects, finding performance overhead ranging from 35% to 150% on test suites.

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