Treating Run-time Execution History as a First-Class Citizen: Co-Versioning Run-time Behavior alongside Code

TL;DR

This paper introduces Behavioral Co-Versioning, a paradigm that couples version control history with a store of run-time behavior observations to improve software evolution analysis.

Contribution

It proposes a new approach to persist and query run-time behavior alongside code revisions, enabling richer analysis of software evolution.

Findings

Feasibility demonstrated with a Python prototype

Behavioral changes detected beyond textual diffs

Archive supports semantic diffing and regression localization

Abstract

Behavioral Co-Versioning remains absent from mainstream practice: while developers routinely version source code with Git, they rarely persist and query how run-time behavior evolves across revisions. This paper argues that this mismatch contributes to a blind spot in software evolution analysis and CI, where rich execution information is discarded and typically reduced to pass/fail outcomes -- despite partial test oracles, flakiness, and silent output or performance drift. We propose \textit{Behavioral Co-Versioning}, a paradigm that couples the Git history with a \textit{Behavioral Archive}: an append-only, queryable store of selected run-time observations (e.g., method I/O and performance signals) collected during test runs and keyed by commit and test context. This enables semantic diffing, behavior-aware regression localization, and retrospective auditing by querying historical executions, complementing proactive, signal-specific monitoring tools. We first outline a minimal data model and change diagnostics based on code/test/behavior fingerprints, and then demonstrate feasibility with a laptop-scale prototype that replays historical commits of a Python project, archives run-time observations in a local Parquet-backed store, and detects behavioral changes not apparent from textual diffs.