CompProv captures detailed calculation steps for verifiable results
CompProv Produces Machine Readable Graphs Encoding Microscopic Algebraic Provenance for Reproducible Computation
Software EngineeringComputational Engineering, Finance, and Science
Summary
When computers produce scientific or financial results, it's important to know exactly how they got those answers to trust them. Existing systems track big steps but miss the tiny math operations that can cause errors. The authors created CompProv, a tool that records every little calculation, storing all details in a graph that can be reviewed later. This lets people check results precisely without sharing secret formulas and helps repeat or test changes safely. They showed it works on finance, measurement, and water modeling examples.
What this means in practice
- •For financial analysts: Trace every calculation step in financial models to verify output correctness without exposing proprietary formulas.
- •For metrology engineers: Reproduce and audit measurement calibrations precisely by recording all intermediate numeric computations under documented input conditions.
Authors
Minas Abramyan, Mohammed Alaa Ala'anzy, Nasir Saeed
Abstract
The reliability of computational results in scientific research and financial modeling increasingly depends on verifiable traceability, not merely on trust in a reported output. Existing provenance systems operate at the granularity of files, datasets, or pipeline stages, leaving the internal sequence of algebraic transformations connecting an algorithm's inputs to its outputs unrecorded; a rounding error, an undocumented substitution, or a missing intermediate value can propagate to a final result with no recoverable trace. This work presents CompProv, a Java-based, audit-oriented provenance framework that captures lineage at the atomic level of individual algebraic operations by encapsulating numerical values in high-precision wrapper objects, producing a serializable Calculation Provenance Graph (CPG) that persists as a self-contained artifact rather than a discarded byproduct. The framework is evaluated through three heterogeneous case studies: a decentralized-finance NAV calculation, a reconstruction of an interferometric gauge-block calibration in metrology under explicitly documented input assumptions, and a hydrological model performance evaluation. Deterministic replay reproduced each result exactly in a fresh environment, and CPG-based input substitution supported sensitivity analysis without exposing the underlying source code. These results indicate that, provided the CompProv runtime and wrapper classes are available in the replay environment, a CPG allows numerical integrity to be audited without disclosing proprietary business logic, and that its self-contained structure supports temporal auditability once an execution environment has become deprecated. This framework establishes an operation-level foundation for auditable-by-design computational systems, with scaling to high-throughput computing identified as a direction for future work.