Create an AI system that provides advanced gas optimization beyond simple pattern matching:
- Machine Learning Gas Prediction: Predict gas costs with high accuracy
- Optimization Strategy Learning: Learn which optimizations work best for different patterns
- Cross-Function Optimization: Optimize gas across multiple functions
- Storage Layout Optimization: AI-optimized storage slot packing
- Call Stack Optimization: Optimize external call patterns
- Deployment Cost Analysis: Analyze and optimize deployment costs
- Runtime vs Tradeoff Analysis: Balance gas optimization with other factors
- Historical Gas Analysis: Track gas usage over time and identify trends
Requirements:
- Build gas cost prediction models using ML
- Create optimization strategy recommendation engine
- Implement storage layout optimization algorithms
- Build call graph analysis for optimization opportunities
- Create deployment cost analysis
- Implement multi-objective optimization (gas vs security vs readability)
- Build historical gas tracking and analysis
- Create integration with gas profiling tools
- Implement A/B testing for optimization effectiveness
- Build gas optimization dashboard and reporting
Acceptance Criteria:
- Gas prediction accuracy within 5% of actual costs
- Achieve 15%+ gas savings over baseline optimizations
- Storage layout optimization reduces storage costs by 20%
- Deployment cost analysis accuracy >90%
- Multi-objective optimization considers tradeoffs appropriately
- Historical analysis identifies meaningful trends
- Integration with existing ChainProof gas analysis
Difficulty: Medium-High - Requires ML expertise, EVM gas modeling
Maintainer Scope Upgrade
This issue is being expanded into a substantial, production-quality ChainProof enhancement. The implementation should be designed as maintainable platform work, not as a narrow proof of concept. A successful pull request must provide a cohesive user-facing capability, typed internal APIs, robust tests, documentation, and CI-safe behavior.
Expanded Objective
Create an AI-assisted gas optimization engine that ranks opportunities by impact, safety, and confidence. The feature should fit the existing monorepo architecture, reuse current scanner/report/CLI patterns where appropriate, and avoid introducing ad hoc subsystems that are difficult to test or maintain.
Required Implementation Depth
This issue is intentionally scoped to require more than 700 lines of meaningful implementation work. The line count expectation applies to purposeful source, tests, fixtures, and documentation that are necessary to deliver the feature. It must not be satisfied through generated output, lockfile churn, formatting-only changes, duplicated boilerplate, or artificial padding.
Expected work includes:
- Static gas-pattern extraction paired with optional LLM explanations and patch suggestions.
- Benchmark fixtures and before/after estimation output for common Solidity inefficiencies.
- CLI/report integration with tests for determinism, provider failures, and suggestion quality gates.
- Public or internal types/interfaces where they clarify behavior and reduce future integration risk.
- Failure-mode handling for invalid input, missing configuration, unavailable optional dependencies, and degraded execution paths.
- Documentation updates that explain how maintainers and users should operate the new capability in local and CI environments.
Professional Quality Bar
The implementation must be production-ready and reviewable in isolation. Contributors should include clear separation between parsing, analysis, reporting, CLI/action integration, and persistence or provider code where those concerns apply. The code should follow existing ChainProof conventions, keep behavior deterministic in tests, and avoid coupling core analysis to network-only services unless explicitly optional and mocked.
Acceptance Criteria
- The delivered PR contains more than 700 meaningful lines of implementation across source, tests, fixtures, and docs, excluding generated files and lockfile-only changes.
- The feature is integrated into the relevant package entrypoints, CLI commands, report formats, GitHub Action behavior, or documentation as appropriate for this issue.
- Unit tests cover normal operation, edge cases, invalid inputs, and at least one realistic fixture or end-to-end workflow.
- Any optional external service, model provider, database, or platform integration has deterministic mocks or fallbacks so CI does not depend on secrets or network availability.
- User-facing output is documented and stable enough for downstream automation.
- Backward compatibility is preserved unless the PR explicitly documents a migration path and the maintainer approves it.
Mandatory CI and Merge Requirements
A PR resolving this issue must not be merged until all repository CI checks pass. At minimum, reviewers should verify the following from a clean checkout:
npm cinpm run lintnpm run build --workspaces --if-presentnpm run test:ci --workspace=packages/corenpm test --workspaces --if-presentnpm run build --workspace=packages/core && npm run docs --workspace=packages/core- Any package-specific tests, examples, validators, or integration checks introduced by the PR
If the PR adds a GitHub Action, report format, dashboard, persistence layer, or external integration, it must also include CI coverage or a documented local verification command for that path. Known warnings are acceptable only when they are documented and do not hide failures.
Review Expectations
Reviewers should reject PRs that only stub APIs, add superficial wrappers, omit tests for critical behavior, rely on live secrets in CI, or meet the line-count target through non-functional bulk changes. The preferred solution is a focused but complete vertical slice that leaves ChainProof more reliable, easier to operate, and easier to extend.
Create an AI system that provides advanced gas optimization beyond simple pattern matching:
Requirements:
Acceptance Criteria:
Difficulty: Medium-High - Requires ML expertise, EVM gas modeling
Maintainer Scope Upgrade
This issue is being expanded into a substantial, production-quality ChainProof enhancement. The implementation should be designed as maintainable platform work, not as a narrow proof of concept. A successful pull request must provide a cohesive user-facing capability, typed internal APIs, robust tests, documentation, and CI-safe behavior.
Expanded Objective
Create an AI-assisted gas optimization engine that ranks opportunities by impact, safety, and confidence. The feature should fit the existing monorepo architecture, reuse current scanner/report/CLI patterns where appropriate, and avoid introducing ad hoc subsystems that are difficult to test or maintain.
Required Implementation Depth
This issue is intentionally scoped to require more than 700 lines of meaningful implementation work. The line count expectation applies to purposeful source, tests, fixtures, and documentation that are necessary to deliver the feature. It must not be satisfied through generated output, lockfile churn, formatting-only changes, duplicated boilerplate, or artificial padding.
Expected work includes:
Professional Quality Bar
The implementation must be production-ready and reviewable in isolation. Contributors should include clear separation between parsing, analysis, reporting, CLI/action integration, and persistence or provider code where those concerns apply. The code should follow existing ChainProof conventions, keep behavior deterministic in tests, and avoid coupling core analysis to network-only services unless explicitly optional and mocked.
Acceptance Criteria
Mandatory CI and Merge Requirements
A PR resolving this issue must not be merged until all repository CI checks pass. At minimum, reviewers should verify the following from a clean checkout:
npm cinpm run lintnpm run build --workspaces --if-presentnpm run test:ci --workspace=packages/corenpm test --workspaces --if-presentnpm run build --workspace=packages/core && npm run docs --workspace=packages/coreIf the PR adds a GitHub Action, report format, dashboard, persistence layer, or external integration, it must also include CI coverage or a documented local verification command for that path. Known warnings are acceptable only when they are documented and do not hide failures.
Review Expectations
Reviewers should reject PRs that only stub APIs, add superficial wrappers, omit tests for critical behavior, rely on live secrets in CI, or meet the line-count target through non-functional bulk changes. The preferred solution is a focused but complete vertical slice that leaves ChainProof more reliable, easier to operate, and easier to extend.