GraphCanon updated Oct 7, 2026 · GitHub synced Oct 7, 2026
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Decision brief
Self-repair provides tools to replicate research experiments on code generation systems self-reparation techniques.
Good fit when
- When aiming to replicate specific experimental data from the ICLR 2024 paper on self-repair in code generation
- If your project requires evaluation of pre-existing datasets like HumanEval for new self-repair strategies
Avoid when
- For creating entirely new datasets or conducting large-scale human studies, as this repository focuses on replicating existing experiments
- When the internal dependencies or incomplete code functions such as exec_sample in APPS are critical for your application without modification
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Archived (887d since push)
- As of Oct 7, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 7, 2026
- Security (OSV)
- No criticals
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install self-repair PyPISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Source code and scripts to replicate experiments from the ICLR 2024 paper investigating whether self-repair is effective in code generation systems.
Capability facts
- Languages
- python
Source: github.language · Oct 7, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 7, 2026)
- `analysis/sample-and-estimate.py`: Python script to generate bootstrapped estimates of pass rates at various budgets.Source link
Tags
README
[ICLR 2024]: Is Self Repair a Silver Bullet for Code Generation? This is is the accompanying repository for the paper Is Self Repair a Silver Bullet for Code Generation?, presented at the Twelfth International Conference on Learning Representations (Vienna, May 2024). It contains...
For agents
This page has a .md twin and JSON over the API.