[Re] Effective Program Debloating via Reinforcement Learning

For a benign evaluator-authored toy program, how much reduction is possible without violating a held-out behavioral test suite?

Run only in a qualified isolated build path; platform-controlled held-out tests and size accounting judge the reduced program, not the submitted test log.

What you could produce

• Versioned protocol, input and environment manifest, and independent per-case comparison table including uncertainty and incomplete cases.

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• Exact article-specific dependencies, data and build requirements remain uninspected.

Limits to keep in view

• No research code was executed; no independent scientific verification has been performed.
• The current qualified pilot is self-contained Python 3.13 with a 90-second author deadline. This article's environment has not been qualified for that path.
• Published source metadata and a historical review do not establish compatibility, successful reproduction, operator independence or current scientific correctness.

Source and permission context

Maurel, Denis; Fillioux, Jérôme; Gugenheim, Dan. [Re] Effective Program Debloating via Reinforcement Learning. ReScience C 8(1), #1; 10.5281/zenodo.6255131.

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Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.

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