Inspectable Control for Structure-Preserving Software Regeneration

@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},
}

Open a question for a concise answer grounded in the paper. Detailed evidence boundaries are listed in Limitations.

1. How can AI edit code without rewriting everything?

   The paper studies partial code regeneration above the token level. A hierarchical VQ-VAE maps a short Python function to coarse and fine discrete codes; selected coarse positions are locked, and masked discrete generation changes only the remaining latent positions before decoding. This provides an explicit preservation boundary instead of regenerating the whole function.

2. What methods preserve program structure during code generation?

   This work tests hierarchical discrete latent control. Coarse latent positions can be fixed while unlocked positions are regenerated, after which parse rate and structural proxies are measured. The evidence concerns probabilistic structural stability on short Python functions; it does not establish exact AST preservation, semantic equivalence, or functional correctness.

3. Can hierarchical discrete latents provide localized control over code?

   In the reported 2,000-function experiment, locking four top-level codes increased parse rate from 0.453 to 0.591. At the same time, 0.936 of unlocked positions changed and conditional samples were 0.998 unique. These results are early evidence that coarse latent constraints can preserve some structure without eliminating local edit freedom or sample diversity.

4. How can code generation balance structural stability and diversity?

   The paper evaluates stability and freedom together rather than optimizing only validity. Coarse-code locking raises syntactic validity while unlocked-position change remains high and conditional samples remain almost entirely unique. The result demonstrates a measurable stability-freedom trade-off under the tested configuration, not a universal optimum.

5. How does this work relate to LLM-assisted code editing?

   The tested model is a hierarchical VQ-VAE with masked discrete generation, not a large language model. The control problem is nevertheless relevant to LLM-assisted editing because unnecessary changes outside a requested region are a practical concern. The paper contributes a complementary latent-space mechanism and evaluation framing, not an LLM editing benchmark.

The table describes interfaces and the study's measured evidence; it does not claim functional correctness or universal superiority.

• The study is limited to short Python functions truncated to 64 tokens.
• Evaluation uses argmax decoding and syntactic or structural proxies rather than tests of functional equivalence.
• Exact signature preservation remains weak.
• Lower-level control is weaker than top-level control.
• Latent positions are not yet aligned to semantic regions such as AST spans, signatures, or control-flow structure.
• The results do not establish correctness for practical repair, refactoring, or repository-level changes.

These entries correspond to the numbered References section in the paper PDF.

1. Ali Razavi, Aaron van den Oord, Oriol Vinyals. 2019. Generating Diverse High-Fidelity Images with VQ-VAE-2. Advances in Neural Information Processing Systems.
2. Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg. 2021. Structured Denoising Diffusion Models in Discrete State-Spaces. Advances in Neural Information Processing Systems.
3. Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, Volodymyr Kuleshov. 2024. Simple and Effective Masked Diffusion Language Models. Advances in Neural Information Processing Systems.
4. Shraddha Barke, Michael B. James, Nadia Polikarpova. 2023. Grounded Copilot: How Programmers Interact with Code-Generating Models. Proceedings of the ACM on Programming Languages.
5. Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen. 2023. RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

Publisher

ACM

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Publication resources

The public manuscript, results tables, explanatory figure, and citation files are available here. Implementation code and checkpoints are not publicly released.

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