refactor-analysis

name	refactor-analysis
description	Automatically scans the TizenFX codebase on a rotating schedule to discover .NET 8 / C# 12+ refactoring targets and register them as GitHub Issues.

TizenFX Refactoring Target Discovery and Issue Registration Pipeline

Overview

A pipeline that scans the TizenFX codebase for .NET 8 and C# 12+ modernization/optimization opportunities, drafts concrete application plans, and automatically registers them as GitHub Issues. Each created issue explicitly states that it is a refactoring objective.

Repository

• Repo: samsung/TizenFX (GitHub)
• CLI: gh CLI (authenticated)

---

Pipeline Flow

① Auto-select target area and scan → ② Optimization feasibility analysis (Self-Reflection)
  → [Pass] ③ Draft refactoring application plan → ④ Auto-register GitHub Issue
  → [Reject] Discard (low ROI / high risk)

---

Stage ①: Auto-select Target Area and Scan

Automatic directory rotation logic: Because the scan scope is broad, the scan area is automatically rotated on every run.

1. First run gh repo clone samsung/TizenFX, or git pull the existing clone to sync
2. Collect the main namespace directories under src/:

   ls -d src/Tizen.*/
   

3. Check which directories have already been analyzed by inspecting existing ai-task labeled issues:

   gh issue list --repo samsung/TizenFX --label "ai-task" --state all --json title --jq '.[].title' | grep -oP '\[Scope: [^\]]+\]'
   

4. Prioritize directories that have not yet been scanned, but once every directory has been covered once, re-scan starting with the one scanned longest ago
5. Scan only 1–2 directories per run (to allow for in-depth analysis)

Scan Philosophy — 4 Evaluation Lenses (lens-based, not category-based)

Rather than "hunting" for a fixed list of patterns, observe the code through 4 evaluation lenses. A single finding may hit multiple lenses at once; the more it overlaps, the higher its grade.

In priority order:

🚀 Lens 1: Performance (Highest Priority)

The most important lens. Target measurable performance improvements.

• Reduce allocations: ToList/ToArray in hot paths, new inside loops, boxing/unboxing, closure allocation (lambda capture)
• Buffer handling: opportunities to apply Span<T>/Memory<T>, ArrayPool<T>, stackalloc (small fixed size)
• New .NET 8 types: FrozenDictionary/FrozenSet (read-only lookup), SearchValues<T> (char search), CollectionsMarshal
• Task optimization: apply ValueTask (hot path), use IAsyncEnumerable, remove .Result/.Wait() (also prevents deadlocks)
• LOH / GC pressure: allocation patterns for arrays ≥85KB, short-lived large objects

🆕 Lens 2: .NET 8 / C# 12 Modernization

Modernize with the latest language and runtime features.

• C# 12: Collection expressions [1, 2, ..x], primary constructors (class/struct), ref readonly parameters
• C# 11: required members, generic math, raw string literals """..."""
• C# 10~9: file-scoped namespaces, record struct, switch expression, init accessor
• .NET 8 APIs: ArgumentException.ThrowIfNull(x), ArgumentOutOfRangeException.ThrowIfNegative, TimeProvider
• Nullable reference types: introduce #nullable enable, ensure NRT consistency

🧹 Lens 3: Clean Code

Improve readability and maintainability (within internal/private scope).

• Eliminate duplication (DRY), extract common logic
• Split methods that violate single responsibility (one method doing too much)
• Reduce deep nesting / complexity (guard clauses, early return)
• Remove dead code, unused private members
• Improve naming — limited to internal/private only (public API renames forbidden)

📐 Lens 4: .NET Coding Guidelines

Adherence to official guidelines. Based on Microsoft Design Guidelines.

• async/await consistency: remove .Result, .Wait() (cause deadlocks)
• IDisposable pattern: using declaration, consistent propagation
• Exception handling: overly broad catch (Exception), swallowed exceptions, mismatched exception types
• Naming conventions (public APIs are immutable; only internals are in scope)

Lens overlap → grade ↑: if a finding hits both Performance and Modernization, it ranks higher than a single-lens finding (see Stage ②).

Quantitative heuristics (to minimize subjective judgment):

• switch conversion candidates: 5+ case branches, each ≥ 3 lines
• allocation inside loops: new or LINQ chain inside for/foreach
• heavy methods: cyclomatic complexity ≥ 10, or ≥ 100 lines

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Stage ②: Optimization Feasibility Analysis and Grade Assignment

2-1. Safety Gate

For each finding, self-verify against the following criteria:

1. Can the Public API Signature be preserved? (no signature changes)
2. Can the Public API Behavior be preserved? (no behavior changes)
3. For Performance-lens findings, is a meaningful benchmark benefit expected?
4. Does it avoid affecting Native Interop / P/Invoke marshalling?
5. Does it maintain Tizen API Level 12+ compatibility? (when using new .NET APIs, confirm support per Tizen API Level)

→ Fails even one of these → discard (low ROI / high risk)

2-2. Grade Assignment

For items passing the gate, assign a grade using this matrix.

Grade	Condition	Action
🔴 Critical	Performance lens + hot path + measurable numerical improvement expected, OR bug risk (async deadlock, missing IDisposable, NullReferenceException path)	Discover ✓ prioritize PR
🟡 Improvement	Modernization / Clean Code / Coding Guidelines lens with clear readability/maintainability gains, OR a Performance improvement that is not on a hot path	Discover ✓
🟢 Nice-to-have	Style preference, plain naming taste, marginal theoretical improvement, weak lens match	Exclude (do not register)

Grade tie-breaking rules:

• If 🔴 Critical is claimed without measurable numerical evidence → downgrade to 🟡 Improvement
• Two or more overlapping lenses can promote the grade by one step
  • e.g., Modernization only → 🟡, but Performance + Modernization → consider 🔴
• Bug risk accompanied (async deadlock, swallowed exception, missing dispose, etc.) → auto-🔴
• When in doubt, classify as 🟢 conservatively (exclude from discovery). Over-discovery is worse than under-discovery.

"Hot path" judgment guide:

• Rendering loops, event handlers, frequently called property getters/setters
• User interaction paths (touch/gesture/layout)
• Initialization paths are not hot paths (avoid applying performance criteria to one-shot code)

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Stage ③: Draft Refactoring Application Plan

For items that pass, author a Markdown plan.

Required plan structure:

[Type: Refactoring]
[Scope: {scanned directory name, e.g., src/Tizen.NUI}]
[Priority: 🔴 Critical | 🟡 Improvement]
[Lens: Performance, Modernization, Clean Code, Coding Guidelines] (list all applicable lenses)

## Observation
{Describe the current state of the code and the problem pattern}

## Problem
{Why refactoring is needed — describe from each applicable lens's perspective}

## Proposed Improvement
{Concrete refactoring approach with Before/After code snippets}

### Target Files
- `{file path 1}`
- `{file path 2}`

## Expected Impact (Quantitative Metrics)
{**Required** for 🔴 Critical; write if possible for 🟡 Improvement}
- e.g., `allocation per call: 3 → 0`, `execution time: ~120ns → ~80ns`, `LOH allocations: eliminated`
- For non-Performance lenses: readability/complexity metrics (cyclomatic complexity, LOC, etc.)

## API Compatibility Check
- Public API signature change: none / {description}
- Behavior change: none / {description}
- Tizen API Level floor: {maintained / raise required — specify}

## Impact Scope
- Number of call sites for the modified symbol (measured via rg): `approx. N locations`
- Distinguish impact within the same assembly vs. other assemblies
- If >100 sites, recommend splitting the scope

Important:

• The top of the body must include [Type: Refactoring], [Priority: ...], and [Lens: ...] so that the downstream agent (refactor-execute) can recognize the mode/priority.
• If 🔴 Critical has no quantitative metric in "Expected Impact", send it back to Stage ② for re-evaluation and downgrade to 🟡.

---

Stage ④: GitHub Issue Auto-Registration

Duplicate check (required before registration):

gh issue list --repo samsung/TizenFX --label "ai-task" --state open --json title,body --jq '.[] | .title + " " + .body'

→ Skip registration if an issue for the same file/pattern already exists

Issue registration:

gh issue create --repo samsung/TizenFX \
  --title "[AI Refactoring] {finding name} [Scope: {directory}]" \
  --body-file issue_plan.md \
  --label "ai-task"

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Constraints

• Create at most 5 issues per run (🔴 Critical first, then 🟡 Improvement)
• 🟢 Nice-to-have items are not registered (excluded at discovery)
• 🔴 Critical grade must include a quantitative metric (expected impact figures). Without one, downgrade to 🟡 or exclude.
• Scan priority: Performance > Modernization > Clean Code > Coding Guidelines (lens order)
• For items hitting multiple lenses, list all applicable lenses in [Lens: ...] in the issue body
• Public API signature/behavior changes are forbidden (must pass the safety gate)
• Tizen API Level 12+ compatibility must be maintained
• Duplicate issues must not be registered (always verify existing issues before registering)
• Include the scanned directory and date in the issue title so scans can be traced

Reporting

• Scan info: directories scanned in this run, directories scheduled for the next run
• Grade distribution: 🔴 Critical N / 🟡 Improvement M (registered counts)
• Lens distribution: Performance X, Modernization Y, Clean Code Z, Coding Guidelines W (overlap counted)
• Created issue list: number, title, link, grade, lenses
• Discard summary:
  • Safety gate failures: N (classified by reason: API signature / behavior / Tizen API Level / interop / negligible impact)
  • 🟢 Nice-to-have classifications: M (brief reason)
• Anomalies: number of items that claimed 🔴 Critical but were downgraded to 🟡 due to missing quantitative metrics (if any)