Mipverify.Jl
B+ 88 completed
Other
unknown / markdown · small
90
Files
9,560
LOC
0
Frameworks
5
Languages
Pipeline State
completedRun ID
#391635Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
36.17Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47250
Member of a group with 2 similar repo(s) — canonical #110064 view group →
Top concepts (2)
Project DescriptionDocumentation
Repobility · code-quality intelligence platform · https://repobility.com
AI Prompt
Create a Julia package called MIPVerify.jl designed for evaluating the robustness of neural networks using Mixed Integer Programming (MIP). The tool should allow users to determine the minimum adversarial distortion or the adversarial test accuracy for a given test input. Include documentation and examples, perhaps using Jupyter notebooks, to guide users through installation and usage, referencing the companion repository for model loading examples.
julia mip machine-learning neural-networks robustness optimization package
Generated by gemma4:latest
Catalog Information
[pkgeval-img]: https://juliaci.github.io/NanosoldierReports/pkgeval_badges/M/MIPVerify.svg [pkgeval-url]: https://juliaci.github.io/NanosoldierReports/pkgeval_badges/M/MIPVerify.html
Description
[pkgeval-img]: https://juliaci.github.io/NanosoldierReports/pkgeval_badges/M/MIPVerify.svg [pkgeval-url]: https://juliaci.github.io/NanosoldierReports/pkgeval_badges/M/MIPVerify.html
Novelty
3/10Tags
julia mip machine-learning neural-networks robustness optimization package
Claude Models
claude-opus-4-6
Quality Score
B+
88.2/100
Structure
89
Code Quality
100
Documentation
73
Testing
85
Practices
78
Security
100
Dependencies
60
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (3500% test-to-source ratio)
- Consistent naming conventions (snake_case)
- Low average code complexity \u2014 well-structured code
- Good security practices \u2014 no major issues detected
- Properly licensed project
Recommendations
- Add a linter configuration to enforce code style consistency
Security & Health
4.1h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
MIT
License
0.0%
Duplication
Languages
Frameworks
None detected
Concepts (2)
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| auto_description | Project Description |  [![PkgEval][pkgeval-img]][pkgeval-url]  | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Documentation | docs | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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