---
pretty_name: "Inspectable Control for Structure-Preserving Software Regeneration — reported result summary"
language:
- en
tags:
- "tabular"
- "research-results"
- "academic-paper"
- "open-access"
- "full-text"
- "inspectable-control"
- "software-artifact-control"
- "hierarchical-discrete-latents"
- "structure-preserving-regeneration"
- "partial-code-regeneration"
- "masked-discrete-generation"
- "software-engineering"
- "automatic-programming"
- "software-maintenance-tools"
- "controllable-code-editing"
- "localized-code-regeneration"
- "structure-preserving-code-generation"
- "hierarchical-discrete-latent-representations"
size_categories:
- n<1K
license: cc-by-4.0
configs:
- config_name: default
  default: true
  data_files:
  - split: summary
    path: results.csv
---

# Inspectable Control for Structure-Preserving Software Regeneration — reported result summary

This repository contains an author-maintained, machine-readable summary of the key quantitative values reported in **Inspectable Control for Structure-Preserving Software Regeneration**.

> Scope: this is a small table-level result summary. It is not the underlying training corpus, evaluation corpus, model code, checkpoint, benchmark release, or a new experimental run.

## Publication

- Canonical publication page: https://aogavrilov.com/publications/inspectable-control/
- DOI: https://doi.org/10.1145/3803437.3807386
- Authors: Alexey Gavrilov, Alan-Barsag Gazzaev, Mikhail Mozikov, Ilya Makarov, Sergey Muravyov
- Venue: Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering
- Searchable full-text HTML: https://aogavrilov.com/publications/inspectable-control/full-text/
- Source-derived full-text Markdown: https://aogavrilov.com/publications/inspectable-control/full-text/index.md
- Source-derived NISO JATS 1.4 XML: https://aogavrilov.com/publications/inspectable-control/full-text/article.jats.xml
- External Hub mirror: [PDF](./paper.pdf) · [full-text Markdown](./article.md) · [NISO JATS XML](./article.jats.xml)
- Open result section: https://aogavrilov.com/publications/inspectable-control/#results
- Hugging Face collection: https://huggingface.co/collections/aogavrilov/publication-result-summaries-6a6b733b3116a87befb37ec2

## Reader guides

- English paper guide: https://aogavrilov.com/publications/inspectable-control/
- Руководство на русском: https://aogavrilov.com/ru/publications/inspectable-control/
- 简体中文论文导读: https://aogavrilov.com/zh/publications/inspectable-control/
- 한국어 논문 가이드: https://aogavrilov.com/ko/publications/inspectable-control/
- Problem-first guide — How can AI edit code without regenerating the entire program?: [English](https://aogavrilov.com/projects/discrete-latent-generation/) · [Русский](https://aogavrilov.com/ru/projects/discrete-latent-generation/) · [简体中文](https://aogavrilov.com/zh/projects/discrete-latent-generation/) · [한국어](https://aogavrilov.com/ko/projects/discrete-latent-generation/)
- Machine-readable research indexes: [English](https://aogavrilov.com/llms.txt) · [Русский](https://aogavrilov.com/ru/llms.txt) · [简体中文](https://aogavrilov.com/zh/llms.txt) · [한국어](https://aogavrilov.com/ko/llms.txt)

## Focused evidence notes

These maintained notes answer narrower problem-first questions and keep the paper’s evidence boundary explicit:

- [Localized code modification with generative models](https://aogavrilov.com/research-notes/localized-code-modification-generative-models/) — How to prevent unnecessary whole-function rewriting while preserving enough freedom for a generative model to make the requested code change. [Markdown](https://aogavrilov.com/research-notes/localized-code-modification-generative-models/index.md)
- [Constrained code generation for software engineering](https://aogavrilov.com/research-notes/constrained-code-generation-software-engineering/) — A practical distinction between grammar constraints, type constraints, preservation boundaries, and behavior-level acceptance checks for generated code. [Markdown](https://aogavrilov.com/research-notes/constrained-code-generation-software-engineering/index.md)
- [AI-assisted refactoring: methods and evidence](https://aogavrilov.com/research-notes/ai-assisted-refactoring-evidence/) — How to evaluate recent AI-assisted refactoring methods without confusing a plausible generated patch with verified behavior preservation. [Markdown](https://aogavrilov.com/research-notes/ai-assisted-refactoring-evidence/index.md)
- [Predictable code generation needs a preservation contract](https://aogavrilov.com/research-notes/predictable-code-generation-preservation-contract/) — Why deterministic sampling is not enough, and how observable protected properties and acceptance checks make code-generation behavior testable. [Markdown](https://aogavrilov.com/research-notes/predictable-code-generation-preservation-contract/index.md)

## Files

- `results.csv` — table shown in the Dataset Viewer.
- `results.json` — table plus DOI, metric, sample-size, condition, uncertainty, and takeaway metadata.
- `results.md` — human-readable result summary.
- `citation.bib` — BibTeX record for the paper.
- `paper.pdf` — CC BY 4.0 author camera-ready manuscript.
- `article.md` — source-derived searchable full text.
- `article.jats.xml` — source-derived NISO JATS 1.4 full text; not publisher XML.
- `manifest.json` and `SHA256SUMS` — source links and integrity metadata for this export.

## Experimental scope recorded by the paper

- Source data: A preprocessed subset of CodeParrot Clean containing 2,000 Python functions.
- Reported sample size: 2,000 preprocessed Python functions; conditional sample uniqueness is 0.998.
- Conditions: 64-token functions, argmax decoding, 16 top-level codes and 32 lower-level codes; full locking exactly recovers the codec reconstruction.
- Metrics: Parse rate; Skeleton and signature preservation proxies; Unlocked-position change rate; Sample uniqueness and entropy
- Main reported takeaway: Coarse latent locking improves syntactic stability without collapsing change in the editable region; the result demonstrates structural control, not guaranteed functional equivalence.

## Limitations

- Statistical uncertainty: The two-page study reports point estimates without confidence intervals or multi-seed statistical analysis.
- Data boundary: The sample is not representative of repository-scale software, multiple programming languages, or behaviorally verified repair tasks.
- Version boundary: A dataset checksum and immutable snapshot identifier are not reported in the two-page paper.
- Reproducibility boundary: A public installation recipe is not yet available; no inactive Code button is shown.

Do not treat absent values as zero, infer functional correctness from structural proxies, or transfer the reported ranking beyond the stated experimental setting.

## Rights and provenance

The author manuscript and this result-summary export are identified as [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). The upstream data described by the paper retain their own terms.

No dataset files are redistributed by this site; reuse remains subject to the upstream CodeParrot dataset and source-code licenses.

The authoritative context and current rights statement are maintained at https://aogavrilov.com/publications/inspectable-control/#data.

## Citation

```bibtex
@inproceedings{Gavrilov2026InspectableControl,
  title      = {Inspectable Control for Structure-Preserving Software Regeneration},
  author     = {Gavrilov, Alexey and Gazzaev, Alan-Barsag and Mozikov, Mikhail and Makarov, Ilya and Muravyov, Sergey},
  booktitle  = {Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering},
  publisher  = {ACM},
  year       = {2026},
  pages      = {1406--1407},
  doi        = {10.1145/3803437.3807386},
  url        = {https://doi.org/10.1145/3803437.3807386},
  isbn       = {979-8-4007-2636-1},
}
```