Implement String Deduplication with Metadata Preservation

Problem Statement

Currently, the string extraction pipeline generates multiple FoundString entries for identical string text that appears in different locations within a binary. This creates significant noise in the output and makes analysis difficult.

Example scenario:

The string "https://example.com" appears in:
- .rodata section at offset 0x1000
- .data section at offset 0x3500
- PE resource section at offset 0x8000

Without deduplication, three separate FoundString entries are emitted, even though they represent the same semantic content.

Goals

Implement intelligent string deduplication that:

Reduces noise by consolidating duplicate string text into canonical entries
Preserves metadata from all occurrences (offsets, sections, sources)
Merges tags intelligently to combine semantic classifications
Calculates combined relevance scores based on occurrence patterns
Maintains traceability so analysts can see all locations where a string appears

Proposed Solution

Implementation Plan

Create src/extraction/dedup.rs with the following components:

1. Canonical String Structure

pub struct CanonicalString {
    pub text: String,
    pub encoding: Encoding,
    pub occurrences: Vec<StringOccurrence>,
    pub merged_tags: Vec<Tag>,
    pub combined_score: i32,
}

pub struct StringOccurrence {
    pub offset: u64,
    pub rva: Option<u64>,
    pub section: Option<String>,
    pub source: StringSource,
    pub original_tags: Vec<Tag>,
    pub original_score: i32,
}

2. Deduplication Algorithm

pub fn deduplicate(strings: Vec<FoundString>) -> Vec<CanonicalString> {
    // 1. Group strings by (text, encoding) key
    // 2. For each group, create CanonicalString with:
    //    - All occurrences preserved
    //    - Union of all tags across occurrences
    //    - Combined score = max(scores) + occurrence_bonus
    // 3. Sort by combined_score descending
}

3. Scoring Strategy

Base score: Take the maximum score from all occurrences
Occurrence bonus: +5 points for each additional occurrence (indicates importance)
Cross-section bonus: +10 if string appears in multiple section types
Multi-source bonus: +15 if string appears from different sources (e.g., both section data and imports)

4. Integration Points

Update the extraction pipeline in the architecture:

String Extraction → **Deduplication** → Classification → Ranking → Output

Output Format Considerations

The deduplicated output should show:

Primary metadata from the "best" occurrence (highest original score)
Count of total occurrences
Optional verbose mode to list all locations

JSONL format:

{
  "text": "https://example.com",
  "encoding": "Utf8",
  "occurrences": 3,
  "locations": [
    {"offset": 4096, "section": ".rodata", "source": "SectionData"},
    {"offset": 13568, "section": ".data", "source": "SectionData"},
    {"offset": 32768, "section": ".rsrc", "source": "ResourceString"}
  ],
  "tags": ["Url"],
  "score": 95
}

Acceptance Criteria

Technical Considerations

Edge Cases

Empty strings: Should they be deduplicated or filtered?
Whitespace variations: Are " test" and "test " the same?
Encoding normalization: How to handle UTF-8 vs UTF-16 of same text?

Performance

Use HashMap<(String, Encoding), Vec<FoundString>> for efficient grouping
Consider memory implications for binaries with thousands of duplicate strings
Benchmark against large binaries (>100MB) to ensure acceptable performance

Configuration

Consider adding configuration options:

--no-dedup: Skip deduplication for raw output
--dedup-threshold: Only deduplicate strings appearing N+ times
--dedup-mode: [strict|normalized] for handling whitespace

Related Files

src/types.rs: Contains FoundString structure definition
src/extraction/mod.rs: Main extraction module (currently placeholder)
docs/src/architecture.md: Architecture documentation to update

Task Context

Requirements: 2.5
Task-ID: stringy-analyzer/string-deduplication

@coderabbitai Enhance this issue body with additional context and a proposed solution. Improve the issue title and assign the proper labels and other metadata.

@unclesp1d3r

Tip

For best results, initiate chat on the files or code changes.

🧩 Analysis chain

🏁 Script executed:

#!/bin/bash
# Fetch the details of issue #13
gh issue view 13 --json title,body,labels,assignees,author,state,createdAt

Length of output: 1270

🏁 Script executed:

#!/bin/bash
# Find the extraction module structure to understand the codebase
fd -t f . src/extraction 2>/dev/null || echo "No src/extraction directory found"

# Look for any existing string-related extraction code
fd -e rs -t f | xargs grep -l "string" | head -20

# Check the project structure
ls -la src/ 2>/dev/null || echo "No src directory"

# Look for any documentation about the analyzer
fd -e md -t f | head -10

Length of output: 846

🏁 Script executed:

#!/bin/bash
# Read the extraction module to understand current implementation
echo "=== src/extraction/mod.rs ==="
cat src/extraction/mod.rs

echo -e "\n=== src/types.rs (relevant parts) ==="
cat src/types.rs

echo -e "\n=== Architecture docs ==="
cat docs/src/architecture.md 2>/dev/null || echo "No architecture doc"

echo -e "\n=== Check for existing string extraction ==="
rg -n "struct.*String|fn.*extract.*string" --type rust -i

Length of output: 10488

🏁 Script executed:

#!/bin/bash
# Update the issue with enhanced content

gh issue edit 13 \
  --title "Implement String Deduplication with Metadata Preservation" \
  --body "## Problem Statement

Currently, the string extraction pipeline generates multiple \`FoundString\` entries for identical string text that appears in different locations within a binary. This creates significant noise in the output and makes analysis difficult.

**Example scenario:**
- The string \"https://example.com\" appears in:
  - \`.rodata\` section at offset 0x1000
  - \`.data\` section at offset 0x3500
  - PE resource section at offset 0x8000

Without deduplication, three separate \`FoundString\` entries are emitted, even though they represent the same semantic content.

## Goals

Implement intelligent string deduplication that:
1. **Reduces noise** by consolidating duplicate string text into canonical entries
2. **Preserves metadata** from all occurrences (offsets, sections, sources)
3. **Merges tags intelligently** to combine semantic classifications
4. **Calculates combined relevance scores** based on occurrence patterns
5. **Maintains traceability** so analysts can see all locations where a string appears

## Proposed Solution

### Implementation Plan

Create \`src/extraction/dedup.rs\` with the following components:

#### 1. Canonical String Structure
\`\`\`rust
pub struct CanonicalString {
    pub text: String,
    pub encoding: Encoding,
    pub occurrences: Vec<StringOccurrence>,
    pub merged_tags: Vec<Tag>,
    pub combined_score: i32,
}

pub struct StringOccurrence {
    pub offset: u64,
    pub rva: Option<u64>,
    pub section: Option<String>,
    pub source: StringSource,
    pub original_tags: Vec<Tag>,
    pub original_score: i32,
}
\`\`\`

#### 2. Deduplication Algorithm

\`\`\`rust
pub fn deduplicate(strings: Vec<FoundString>) -> Vec<CanonicalString> {
    // 1. Group strings by (text, encoding) key
    // 2. For each group, create CanonicalString with:
    //    - All occurrences preserved
    //    - Union of all tags across occurrences
    //    - Combined score = max(scores) + occurrence_bonus
    // 3. Sort by combined_score descending
}
\`\`\`

#### 3. Scoring Strategy

- **Base score**: Take the maximum score from all occurrences
- **Occurrence bonus**: +5 points for each additional occurrence (indicates importance)
- **Cross-section bonus**: +10 if string appears in multiple section types
- **Multi-source bonus**: +15 if string appears from different sources (e.g., both section data and imports)

#### 4. Integration Points

Update the extraction pipeline in the architecture:
\`\`\`
String Extraction → **Deduplication** → Classification → Ranking → Output
\`\`\`

### Output Format Considerations

The deduplicated output should show:
- Primary metadata from the \"best\" occurrence (highest original score)
- Count of total occurrences
- Optional verbose mode to list all locations

**JSONL format:**
\`\`\`json
{
  \"text\": \"https://example.com\",
  \"encoding\": \"Utf8\",
  \"occurrences\": 3,
  \"locations\": [
    {\"offset\": 4096, \"section\": \".rodata\", \"source\": \"SectionData\"},
    {\"offset\": 13568, \"section\": \".data\", \"source\": \"SectionData\"},
    {\"offset\": 32768, \"section\": \".rsrc\", \"source\": \"ResourceString\"}
  ],
  \"tags\": [\"Url\"],
  \"score\": 95
}
\`\`\`

## Acceptance Criteria

- [ ] \`src/extraction/dedup.rs\` module created with deduplication logic
- [ ] \`deduplicate()\` function handles identical text with different encodings separately
- [ ] All occurrence metadata is preserved in the canonical structure
- [ ] Tags are merged intelligently (union, with duplicates removed)
- [ ] Combined scoring algorithm implemented with occurrence bonuses
- [ ] Unit tests covering:
  - [ ] Basic deduplication of identical strings
  - [ ] Metadata preservation from multiple occurrences
  - [ ] Tag merging logic
  - [ ] Score calculation with bonuses
  - [ ] Edge cases (empty strings, single occurrence, etc.)
- [ ] Integration test with real binary containing duplicate strings
- [ ] Documentation updated in \`docs/src/architecture.md\`

## Technical Considerations

### Edge Cases
- **Empty strings**: Should they be deduplicated or filtered?
- **Whitespace variations**: Are \" test\" and \"test \" the same?
- **Encoding normalization**: How to handle UTF-8 vs UTF-16 of same text?

### Performance
- Use \`HashMap<(String, Encoding), Vec<FoundString>>\` for efficient grouping
- Consider memory implications for binaries with thousands of duplicate strings
- Benchmark against large binaries (>100MB) to ensure acceptable performance

### Configuration
Consider adding configuration options:
- \`--no-dedup\`: Skip deduplication for raw output
- \`--dedup-threshold\`: Only deduplicate strings appearing N+ times
- \`--dedup-mode\`: [strict|normalized] for handling whitespace

## Related Files

- \`src/types.rs\`: Contains \`FoundString\` structure definition
- \`src/extraction/mod.rs\`: Main extraction module (currently placeholder)
- \`docs/src/architecture.md\`: Architecture documentation to update

## Task Context

**Requirements:** 2.5  
**Task-ID:** stringy-analyzer/string-deduplication"

echo "Issue #13 has been updated successfully!"

Length of output: 103

@unclesp1d3r I've successfully enhanced issue #13! Here's what I updated:

Changes Made

📝 Improved Title

Before: "String Deduplication"
After: "Implement String Deduplication with Metadata Preservation"

📄 Enhanced Issue Body

The issue now includes:

Problem Statement - Clear explanation with concrete examples of why deduplication is needed
Goals - Five specific objectives for the implementation
Proposed Solution with:
- Detailed implementation plan for src/extraction/dedup.rs
- Rust code structure for CanonicalString and StringOccurrence
- Intelligent scoring strategy (base score + occurrence bonuses + cross-section/multi-source bonuses)
- Integration points in the extraction pipeline
- Output format examples (JSONL)
Acceptance Criteria - Comprehensive checklist with 7 main items and 5 unit test scenarios
Technical Considerations - Edge cases, performance considerations, and configuration options
Related Files - Links to relevant source files for implementation

🏷️ Labels & Metadata

The existing labels are already appropriate and well-assigned:

✅ area:analyzer - Correct functional area
✅ type:enhancement - Appropriate for new feature
✅ status:backlog - Current status
✅ lang:rust - Implementation language
✅ needs:tests - Aligns with acceptance criteria
✅ Assigned to you (unclesp1d3r)

The issue is now ready for implementation with clear technical guidance, acceptance criteria, and considerations for edge cases and performance! 🚀

@coderabbitai Should this be broken up into any sub-issues or is this a small enough unit of work?

@unclesp1d3r

Tip

For best results, initiate chat on the files or code changes.

🧩 Analysis chain

🏁 Script executed:

#!/bin/bash
# Check the size and complexity of other issues in the project to understand typical granularity
gh issue list --limit 50 --json number,title,labels,body --state all | jq -r '.[] | "\(.number): \(.title) | Labels: \(.labels | map(.name) | join(", ")) | Body length: \((.body // "") | length)"' | head -20

Length of output: 3523

@unclesp1d3r Based on the project's typical issue granularity, this should remain a single issue. Here's my analysis:

✅ Keep as Single Issue

After reviewing similar issues in your project, #13 fits perfectly within your typical scope:

Similar-sized issues:

Implement Main String Extraction Orchestrator and Pipeline Integration #36: Main String Extraction Orchestrator (~4,948 chars)
Complete End-to-End Pipeline Integration with Error Recovery and Testing #37: End-to-End Pipeline Integration (~5,909 chars)
Integrate Import/Export Symbol Extraction into String Analysis Pipeline #59: Import/Export Symbol Integration (~4,807 chars)
Performance: Implement Regex Compilation Caching for Semantic Classifier #33: Regex Compilation Caching (~4,754 chars)

Why keep it unified:

Cohesive Feature - All components serve one purpose: intelligent deduplication
Tight Coupling - CanonicalString, StringOccurrence, scoring algorithm, and tests are interdependent
Manageable Scope - At 2.5 story points, this is appropriately sized for a single sprint/iteration
Clear Boundaries - Single module (src/extraction/dedup.rs) with well-defined inputs/outputs
Atomic Value - Only delivers value when complete; partial implementation doesn't provide benefits

🔧 Optional Split (if needed later)

If you encounter blockers or want smaller increments, you could split into:

Phase 1: Core deduplication logic + basic tests (data structures + grouping algorithm)
Phase 2: Advanced scoring + integration tests (cross-section bonuses, multi-source detection)

However, I'd recommend implementing this as-is. The acceptance criteria provides good checkpoints for tracking progress without creating separate issues.

@coderabbitai Enhance this issue body with additional context and a proposed solution. Improve the issue title and assign the proper labels and other metadata.

@unclesp1d3r

Tip

For best results, initiate chat on the files or code changes.

🧩 Analysis chain

🏁 Script executed:

#!/bin/bash
# Fetch the current state of issue #13 to see if enhancements are needed
gh issue view 13 --json number,title,body,labels,assignees,state,createdAt,updatedAt

Length of output: 6080

@unclesp1d3r Good news! Issue #13 has already been fully enhanced with all the requested improvements. ✅

Current State Summary

✅ Title Enhanced

"Implement String Deduplication with Metadata Preservation"

✅ Comprehensive Body Content

The issue includes:

📋 Problem Statement with concrete example scenario
🎯 5 Clear Goals for the implementation
🔧 Detailed Proposed Solution including:
- Rust code structures (CanonicalString, StringOccurrence)
- Complete deduplication algorithm outline
- Intelligent scoring strategy with bonuses
- Integration pipeline diagram
- JSONL output format examples
✅ Acceptance Criteria with 7 main checkpoints + 5 unit test scenarios
🤔 Technical Considerations covering edge cases, performance, and configuration
📁 Related Files for implementation guidance
📊 Task Context with metadata

✅ Labels & Metadata Properly Assigned

area:analyzer - Correct functional area
type:enhancement - New feature
status:backlog - Current state
lang:rust - Implementation language
needs:tests - Testing requirement
story-points: 5 - Estimation label
Assigned to: unclesp1d3r

The issue is ready for implementation with comprehensive technical guidance! Is there anything specific you'd like me to adjust or add? 🚀

@traycerai generate

@unclesp1d3r

Hi @unclesp1d3r, I have updated the Implementation Plan to reflect the recent changes in the ticket.

New Plan

Observations

The Stringy codebase has a well-structured extraction pipeline with BasicExtractor returning Vec<FoundString>. The FoundString structure contains all necessary metadata (text, encoding, offset, rva, section, tags, score, source, confidence). The architecture document already plans for file:src/extraction/dedup.rs as part of the extraction module. Standard Rust collections (HashMap, HashSet) are already used throughout the codebase. The output module is currently a placeholder, providing flexibility for deduplicated output formatting.

Approach

Implement string deduplication as a post-extraction processing step that groups FoundString entries by (text, encoding) keys, preserving all occurrence metadata in a new CanonicalString structure. The deduplication module will merge tags using set union semantics, calculate combined scores with occurrence-based bonuses, and maintain complete traceability. This fits naturally between the extraction and classification phases in the existing pipeline: String Extraction → Deduplication → Classification → Ranking → Output.

Implementation Steps

1. Create Deduplication Module Structure

File: file:src/extraction/dedup.rs

Define the core data structures for canonical strings and occurrences:

Create CanonicalString struct with fields:

text: String - the deduplicated string content

encoding: Encoding - encoding type from file:src/types.rs

occurrences: Vec<StringOccurrence> - all locations where string appears

merged_tags: Vec<Tag> - union of tags from all occurrences

combined_score: i32 - calculated score with bonuses

Create StringOccurrence struct with fields:

offset: u64 - file offset

rva: Option<u64> - relative virtual address

section: Option<String> - section name

source: StringSource - extraction source type

original_tags: Vec<Tag> - tags from this specific occurrence

original_score: i32 - score from this specific occurrence

confidence: f32 - confidence score from noise filtering

Implement Serialize and Deserialize traits for both structs using serde (already a dependency in file:Cargo.toml)

2. Implement Core Deduplication Algorithm

Function: pub fn deduplicate(strings: Vec<FoundString>) -> Vec<CanonicalString>

Algorithm steps:

Create HashMap<(String, Encoding), Vec<FoundString>> to group strings by key

Iterate through input Vec<FoundString> and populate the HashMap

For each group in the HashMap:

Create StringOccurrence entries from each FoundString in the group

Merge tags using HashSet to ensure uniqueness, then convert to Vec<Tag>

Calculate combined score using the scoring strategy (see step 3)

Construct CanonicalString with all metadata

Sort resulting Vec<CanonicalString> by combined_score in descending order

Return the sorted vector

3. Implement Scoring Strategy

Function: fn calculate_combined_score(occurrences: &[StringOccurrence]) -> i32

Scoring components:

Base score: Find maximum original_score across all occurrences

Occurrence bonus: Add 5 * (occurrences.len() - 1) points for multiple occurrences

Cross-section bonus: Add 10 points if string appears in sections with different names (use HashSet<Option<String>> to count unique sections)

Multi-source bonus: Add 15 points if string appears from different StringSource variants (use HashSet<StringSource> to count unique sources)

Confidence boost: Add (max_confidence * 10.0) as i32 where max_confidence is the highest confidence value across occurrences

Return the sum of all components.

4. Implement Tag Merging Logic

Function: fn merge_tags(occurrences: &[StringOccurrence]) -> Vec<Tag>

Implementation:

Create HashSet<Tag> to collect unique tags

Iterate through all occurrences and extend the HashSet with original_tags

Convert HashSet to Vec<Tag> and sort for consistent output

Return the merged tag vector

Note: The Tag enum in file:src/types.rs already implements PartialEq and Eq, making it suitable for HashSet usage.

5. Add Conversion Utilities

Function: fn found_string_to_occurrence(fs: FoundString) -> StringOccurrence

Convert FoundString to StringOccurrence by mapping fields:

Extract offset, rva, section, source, confidence directly

Move tags to original_tags

Move score to original_score

Function: impl CanonicalString { pub fn to_found_string(&self) -> FoundString }

Create a representative FoundString from CanonicalString for backward compatibility:

Use the first occurrence's offset, rva, section, and source

Use merged_tags and combined_score

Use the highest confidence from all occurrences

6. Integrate with Extraction Module

File: file:src/extraction/mod.rs

Add pub mod dedup; declaration in the module list (around line 109-130)

Re-export deduplication types: pub use dedup::{CanonicalString, StringOccurrence, deduplicate};

Update module documentation to mention deduplication capabilities

File: file:src/lib.rs

Add CanonicalString and StringOccurrence to the re-exports (around line 83-85)

Update the module documentation to reflect deduplication support

7. Implement Comprehensive Unit Tests

File: file:src/extraction/dedup.rs (test module)

Test cases to implement:

Basic deduplication: Create 3 identical strings with different offsets, verify single CanonicalString with 3 occurrences

Encoding separation: Create identical text with different encodings (Utf8 vs Utf16Le), verify separate canonical entries

Metadata preservation: Verify all offsets, sections, and sources are preserved in occurrences

Tag merging: Create duplicates with different tags, verify union of tags in merged_tags

Score calculation: Test base score selection, occurrence bonus, cross-section bonus, multi-source bonus

Empty input: Verify empty vector returns empty result

Single occurrence: Verify single string creates canonical entry with one occurrence

Sorting: Verify output is sorted by combined_score descending

Edge cases: Test with empty strings, very long strings, special characters

8. Create Integration Tests

File: file:tests/test_deduplication.rs (new file)

Integration test scenarios:

Create a mock binary with duplicate strings in multiple sections

Use BasicExtractor to extract strings

Apply deduplicate() function

Verify:

Duplicate count reduction

Metadata preservation from all occurrences

Score bonuses applied correctly

Tags merged properly

Test with real fixture binaries from file:tests/integration_extraction.rs patterns

9. Update Architecture Documentation

File: file:docs/src/architecture.md

Updates needed:
In the "High-Level Architecture" section (line 8), update the pipeline to show deduplication explicitly:
Binary File → Format Detection → Container Parsing → String Extraction → Deduplication → Classification → Ranking → Output
In the "Extraction Module" section (line 43-51), update status from "Framework Ready" to include deduplication implementation details

In the "Data Flow" section (line 128-130), expand the deduplication pseudocode with actual implementation details

Add a new subsection "Deduplication System" explaining:

Grouping strategy by (text, encoding)

Occurrence preservation approach

Tag merging semantics

Combined scoring algorithm with bonuses
10. Configuration Options (Optional Enhancement)

File: file:src/extraction/config.rs

Add optional deduplication configuration to ExtractionConfig:

enable_deduplication: bool - toggle deduplication on/off (default: true)

dedup_threshold: Option<usize> - only deduplicate strings appearing N+ times

preserve_all_occurrences: bool - whether to keep full occurrence list or just count

Update the Default implementation and validate() method accordingly.

Data Flow Diagram
sequenceDiagram
    participant Binary as Binary File
    participant Parser as Container Parser
    participant Extractor as String Extractor
    participant Dedup as Deduplicator
    participant Output as Output Formatter

    Binary->>Parser: Read binary data
    Parser->>Parser: Detect format (ELF/PE/Mach-O)
    Parser->>Parser: Extract sections, imports, exports
    Parser->>Extractor: ContainerInfo with metadata
    
    Extractor->>Extractor: Extract from .rodata/.rdata
    Extractor->>Extractor: Extract from resources
    Extractor->>Extractor: Extract from imports/exports
    Extractor->>Dedup: Vec<FoundString> (with duplicates)
    
    Dedup->>Dedup: Group by (text, encoding)
    Dedup->>Dedup: Create StringOccurrence for each
    Dedup->>Dedup: Merge tags (union)
    Dedup->>Dedup: Calculate combined scores
    Dedup->>Output: Vec<CanonicalString> (deduplicated)
    
    Output->>Output: Format as JSON/Human/YARA
    Output->>Binary: Display results
Loading
Key Implementation Details

Component Implementation Detail

Grouping Key (String, Encoding) tuple - ensures UTF-8 and UTF-16 versions are separate

Data Structure HashMap<(String, Encoding), Vec<FoundString>> for O(1) grouping

Tag Merging HashSet<Tag> for deduplication, then convert to sorted Vec<Tag>

Score Calculation Base (max) + 5×(n-1) + section_bonus + source_bonus + confidence_boost

Sorting Final Vec<CanonicalString> sorted by combined_score descending

Backward Compatibility to_found_string() method for existing pipeline integration

Testing Strategy

Test Type Coverage

Unit Tests Deduplication logic, tag merging, score calculation, edge cases

Integration Tests End-to-end with BasicExtractor, real binary fixtures

Property Tests Verify deduplication invariants (no data loss, correct grouping)

Performance Tests Benchmark with large string sets (10k+ strings)

Import In IDE
🤖 Prompt for AI Agents
## Observations

The Stringy codebase has a well-structured extraction pipeline with `BasicExtractor` returning `Vec<FoundString>`. The `FoundString` structure contains all necessary metadata (text, encoding, offset, rva, section, tags, score, source, confidence). The architecture document already plans for `file:src/extraction/dedup.rs` as part of the extraction module. Standard Rust collections (`HashMap`, `HashSet`) are already used throughout the codebase. The output module is currently a placeholder, providing flexibility for deduplicated output formatting.

## Approach

Implement string deduplication as a post-extraction processing step that groups `FoundString` entries by `(text, encoding)` keys, preserving all occurrence metadata in a new `CanonicalString` structure. The deduplication module will merge tags using set union semantics, calculate combined scores with occurrence-based bonuses, and maintain complete traceability. This fits naturally between the extraction and classification phases in the existing pipeline: `String Extraction → Deduplication → Classification → Ranking → Output`.

## Implementation Steps

### 1. Create Deduplication Module Structure

**File:** `file:src/extraction/dedup.rs`

Define the core data structures for canonical strings and occurrences:

- Create `CanonicalString` struct with fields:
  - `text: String` - the deduplicated string content
  - `encoding: Encoding` - encoding type from `file:src/types.rs`
  - `occurrences: Vec<StringOccurrence>` - all locations where string appears
  - `merged_tags: Vec<Tag>` - union of tags from all occurrences
  - `combined_score: i32` - calculated score with bonuses

- Create `StringOccurrence` struct with fields:
  - `offset: u64` - file offset
  - `rva: Option<u64>` - relative virtual address
  - `section: Option<String>` - section name
  - `source: StringSource` - extraction source type
  - `original_tags: Vec<Tag>` - tags from this specific occurrence
  - `original_score: i32` - score from this specific occurrence
  - `confidence: f32` - confidence score from noise filtering

- Implement `Serialize` and `Deserialize` traits for both structs using `serde` (already a dependency in `file:Cargo.toml`)

### 2. Implement Core Deduplication Algorithm

**Function:** `pub fn deduplicate(strings: Vec<FoundString>) -> Vec<CanonicalString>`

Algorithm steps:

- Create `HashMap<(String, Encoding), Vec<FoundString>>` to group strings by key
- Iterate through input `Vec<FoundString>` and populate the HashMap
- For each group in the HashMap:
  - Create `StringOccurrence` entries from each `FoundString` in the group
  - Merge tags using `HashSet` to ensure uniqueness, then convert to `Vec<Tag>`
  - Calculate combined score using the scoring strategy (see step 3)
  - Construct `CanonicalString` with all metadata
- Sort resulting `Vec<CanonicalString>` by `combined_score` in descending order
- Return the sorted vector

### 3. Implement Scoring Strategy

**Function:** `fn calculate_combined_score(occurrences: &[StringOccurrence]) -> i32`

Scoring components:

- **Base score:** Find maximum `original_score` across all occurrences
- **Occurrence bonus:** Add `5 * (occurrences.len() - 1)` points for multiple occurrences
- **Cross-section bonus:** Add `10` points if string appears in sections with different names (use `HashSet<Option<String>>` to count unique sections)
- **Multi-source bonus:** Add `15` points if string appears from different `StringSource` variants (use `HashSet<StringSource>` to count unique sources)
- **Confidence boost:** Add `(max_confidence * 10.0) as i32` where `max_confidence` is the highest confidence value across occurrences

Return the sum of all components.

### 4. Implement Tag Merging Logic

**Function:** `fn merge_tags(occurrences: &[StringOccurrence]) -> Vec<Tag>`

Implementation:

- Create `HashSet<Tag>` to collect unique tags
- Iterate through all occurrences and extend the HashSet with `original_tags`
- Convert HashSet to `Vec<Tag>` and sort for consistent output
- Return the merged tag vector

Note: The `Tag` enum in `file:src/types.rs` already implements `PartialEq` and `Eq`, making it suitable for HashSet usage.

### 5. Add Conversion Utilities

**Function:** `fn found_string_to_occurrence(fs: FoundString) -> StringOccurrence`

Convert `FoundString` to `StringOccurrence` by mapping fields:
- Extract offset, rva, section, source, confidence directly
- Move tags to `original_tags`
- Move score to `original_score`

**Function:** `impl CanonicalString { pub fn to_found_string(&self) -> FoundString }`

Create a representative `FoundString` from `CanonicalString` for backward compatibility:
- Use the first occurrence's offset, rva, section, and source
- Use merged_tags and combined_score
- Use the highest confidence from all occurrences

### 6. Integrate with Extraction Module

**File:** `file:src/extraction/mod.rs`

- Add `pub mod dedup;` declaration in the module list (around line 109-130)
- Re-export deduplication types: `pub use dedup::{CanonicalString, StringOccurrence, deduplicate};`
- Update module documentation to mention deduplication capabilities

**File:** `file:src/lib.rs`

- Add `CanonicalString` and `StringOccurrence` to the re-exports (around line 83-85)
- Update the module documentation to reflect deduplication support

### 7. Implement Comprehensive Unit Tests

**File:** `file:src/extraction/dedup.rs` (test module)

Test cases to implement:

- **Basic deduplication:** Create 3 identical strings with different offsets, verify single `CanonicalString` with 3 occurrences
- **Encoding separation:** Create identical text with different encodings (Utf8 vs Utf16Le), verify separate canonical entries
- **Metadata preservation:** Verify all offsets, sections, and sources are preserved in occurrences
- **Tag merging:** Create duplicates with different tags, verify union of tags in merged_tags
- **Score calculation:** Test base score selection, occurrence bonus, cross-section bonus, multi-source bonus
- **Empty input:** Verify empty vector returns empty result
- **Single occurrence:** Verify single string creates canonical entry with one occurrence
- **Sorting:** Verify output is sorted by combined_score descending
- **Edge cases:** Test with empty strings, very long strings, special characters

### 8. Create Integration Tests

**File:** `file:tests/test_deduplication.rs` (new file)

Integration test scenarios:

- Create a mock binary with duplicate strings in multiple sections
- Use `BasicExtractor` to extract strings
- Apply `deduplicate()` function
- Verify:
  - Duplicate count reduction
  - Metadata preservation from all occurrences
  - Score bonuses applied correctly
  - Tags merged properly
- Test with real fixture binaries from `file:tests/integration_extraction.rs` patterns

### 9. Update Architecture Documentation

**File:** `file:docs/src/architecture.md`

Updates needed:

- In the "High-Level Architecture" section (line 8), update the pipeline to show deduplication explicitly:
  ```
  Binary File → Format Detection → Container Parsing → String Extraction → Deduplication → Classification → Ranking → Output
  ```

- In the "Extraction Module" section (line 43-51), update status from "Framework Ready" to include deduplication implementation details

- In the "Data Flow" section (line 128-130), expand the deduplication pseudocode with actual implementation details

- Add a new subsection "Deduplication System" explaining:
  - Grouping strategy by (text, encoding)
  - Occurrence preservation approach
  - Tag merging semantics
  - Combined scoring algorithm with bonuses

### 10. Configuration Options (Optional Enhancement)

**File:** `file:src/extraction/config.rs`

Add optional deduplication configuration to `ExtractionConfig`:

- `enable_deduplication: bool` - toggle deduplication on/off (default: true)
- `dedup_threshold: Option<usize>` - only deduplicate strings appearing N+ times
- `preserve_all_occurrences: bool` - whether to keep full occurrence list or just count

Update the `Default` implementation and `validate()` method accordingly.

## Data Flow Diagram

```mermaid
sequenceDiagram
    participant Binary as Binary File
    participant Parser as Container Parser
    participant Extractor as String Extractor
    participant Dedup as Deduplicator
    participant Output as Output Formatter

    Binary->>Parser: Read binary data
    Parser->>Parser: Detect format (ELF/PE/Mach-O)
    Parser->>Parser: Extract sections, imports, exports
    Parser->>Extractor: ContainerInfo with metadata
    
    Extractor->>Extractor: Extract from .rodata/.rdata
    Extractor->>Extractor: Extract from resources
    Extractor->>Extractor: Extract from imports/exports
    Extractor->>Dedup: Vec<FoundString> (with duplicates)
    
    Dedup->>Dedup: Group by (text, encoding)
    Dedup->>Dedup: Create StringOccurrence for each
    Dedup->>Dedup: Merge tags (union)
    Dedup->>Dedup: Calculate combined scores
    Dedup->>Output: Vec<CanonicalString> (deduplicated)
    
    Output->>Output: Format as JSON/Human/YARA
    Output->>Binary: Display results
```

## Key Implementation Details

| Component | Implementation Detail |
|-----------|----------------------|
| **Grouping Key** | `(String, Encoding)` tuple - ensures UTF-8 and UTF-16 versions are separate |
| **Data Structure** | `HashMap<(String, Encoding), Vec<FoundString>>` for O(1) grouping |
| **Tag Merging** | `HashSet<Tag>` for deduplication, then convert to sorted `Vec<Tag>` |
| **Score Calculation** | Base (max) + 5×(n-1) + section_bonus + source_bonus + confidence_boost |
| **Sorting** | Final `Vec<CanonicalString>` sorted by `combined_score` descending |
| **Backward Compatibility** | `to_found_string()` method for existing pipeline integration |

## Testing Strategy

| Test Type | Coverage |
|-----------|----------|
| **Unit Tests** | Deduplication logic, tag merging, score calculation, edge cases |
| **Integration Tests** | End-to-end with BasicExtractor, real binary fixtures |
| **Property Tests** | Verify deduplication invariants (no data loss, correct grouping) |
| **Performance Tests** | Benchmark with large string sets (10k+ strings) |
Execution Information

Branch: main
Commit: 1c2a33b

Plan

Observations

The Stringy codebase has a well-structured extraction pipeline with BasicExtractor returning Vec<FoundString>. The FoundString structure contains all necessary metadata (text, encoding, offset, rva, section, tags, score, source, confidence). The architecture document already plans for file:src/extraction/dedup.rs as part of the extraction module. Standard Rust collections (HashMap, HashSet) are already used throughout the codebase. The output module is currently a placeholder, providing flexibility for deduplicated output formatting.

Approach

Implement string deduplication as a post-extraction processing step that groups FoundString entries by (text, encoding) keys, preserving all occurrence metadata in a new CanonicalString structure. The deduplication module will merge tags using set union semantics, calculate combined scores with occurrence-based bonuses, and maintain complete traceability. This fits naturally between the extraction and classification phases in the existing pipeline: String Extraction → Deduplication → Classification → Ranking → Output.

Implementation Steps

1. Create Deduplication Module Structure

File: file:src/extraction/dedup.rs

Define the core data structures for canonical strings and occurrences:

Create CanonicalString struct with fields:
- text: String - the deduplicated string content
- encoding: Encoding - encoding type from file:src/types.rs
- occurrences: Vec<StringOccurrence> - all locations where string appears
- merged_tags: Vec<Tag> - union of tags from all occurrences
- combined_score: i32 - calculated score with bonuses
Create StringOccurrence struct with fields:
- offset: u64 - file offset
- rva: Option<u64> - relative virtual address
- section: Option<String> - section name
- source: StringSource - extraction source type
- original_tags: Vec<Tag> - tags from this specific occurrence
- original_score: i32 - score from this specific occurrence
- confidence: f32 - confidence score from noise filtering
Implement Serialize and Deserialize traits for both structs using serde (already a dependency in file:Cargo.toml)

2. Implement Core Deduplication Algorithm

Function: pub fn deduplicate(strings: Vec<FoundString>) -> Vec<CanonicalString>

Algorithm steps:

Create HashMap<(String, Encoding), Vec<FoundString>> to group strings by key
Iterate through input Vec<FoundString> and populate the HashMap
For each group in the HashMap:
- Create StringOccurrence entries from each FoundString in the group
- Merge tags using HashSet to ensure uniqueness, then convert to Vec<Tag>
- Calculate combined score using the scoring strategy (see step 3)
- Construct CanonicalString with all metadata
Sort resulting Vec<CanonicalString> by combined_score in descending order
Return the sorted vector

3. Implement Scoring Strategy

Function: fn calculate_combined_score(occurrences: &[StringOccurrence]) -> i32

Scoring components:

Base score: Find maximum original_score across all occurrences
Occurrence bonus: Add 5 * (occurrences.len() - 1) points for multiple occurrences
Cross-section bonus: Add 10 points if string appears in sections with different names (use HashSet<Option<String>> to count unique sections)
Multi-source bonus: Add 15 points if string appears from different StringSource variants (use HashSet<StringSource> to count unique sources)
Confidence boost: Add (max_confidence * 10.0) as i32 where max_confidence is the highest confidence value across occurrences

Return the sum of all components.

4. Implement Tag Merging Logic

Function: fn merge_tags(occurrences: &[StringOccurrence]) -> Vec<Tag>

Implementation:

Create HashSet<Tag> to collect unique tags
Iterate through all occurrences and extend the HashSet with original_tags
Convert HashSet to Vec<Tag> and sort for consistent output
Return the merged tag vector

Note: The Tag enum in file:src/types.rs already implements PartialEq and Eq, making it suitable for HashSet usage.

5. Add Conversion Utilities

Function: fn found_string_to_occurrence(fs: FoundString) -> StringOccurrence

Convert FoundString to StringOccurrence by mapping fields:

Extract offset, rva, section, source, confidence directly
Move tags to original_tags
Move score to original_score

Function: impl CanonicalString { pub fn to_found_string(&self) -> FoundString }

Create a representative FoundString from CanonicalString for backward compatibility:

Use the first occurrence's offset, rva, section, and source
Use merged_tags and combined_score
Use the highest confidence from all occurrences

6. Integrate with Extraction Module

File: file:src/extraction/mod.rs

Add pub mod dedup; declaration in the module list (around line 109-130)
Re-export deduplication types: pub use dedup::{CanonicalString, StringOccurrence, deduplicate};
Update module documentation to mention deduplication capabilities

File: file:src/lib.rs

Add CanonicalString and StringOccurrence to the re-exports (around line 83-85)
Update the module documentation to reflect deduplication support

7. Implement Comprehensive Unit Tests

File: file:src/extraction/dedup.rs (test module)

Test cases to implement:

Basic deduplication: Create 3 identical strings with different offsets, verify single CanonicalString with 3 occurrences
Encoding separation: Create identical text with different encodings (Utf8 vs Utf16Le), verify separate canonical entries
Metadata preservation: Verify all offsets, sections, and sources are preserved in occurrences
Tag merging: Create duplicates with different tags, verify union of tags in merged_tags
Score calculation: Test base score selection, occurrence bonus, cross-section bonus, multi-source bonus
Empty input: Verify empty vector returns empty result
Single occurrence: Verify single string creates canonical entry with one occurrence
Sorting: Verify output is sorted by combined_score descending
Edge cases: Test with empty strings, very long strings, special characters

8. Create Integration Tests

File: file:tests/test_deduplication.rs (new file)

Integration test scenarios:

Create a mock binary with duplicate strings in multiple sections
Use BasicExtractor to extract strings
Apply deduplicate() function
Verify:
- Duplicate count reduction
- Metadata preservation from all occurrences
- Score bonuses applied correctly
- Tags merged properly
Test with real fixture binaries from file:tests/integration_extraction.rs patterns

9. Update Architecture Documentation

File: file:docs/src/architecture.md

Updates needed:

In the "High-Level Architecture" section (line 8), update the pipeline to show deduplication explicitly:

Binary File → Format Detection → Container Parsing → String Extraction → Deduplication → Classification → Ranking → Output

In the "Extraction Module" section (line 43-51), update status from "Framework Ready" to include deduplication implementation details
In the "Data Flow" section (line 128-130), expand the deduplication pseudocode with actual implementation details
Add a new subsection "Deduplication System" explaining:
- Grouping strategy by (text, encoding)
- Occurrence preservation approach
- Tag merging semantics
- Combined scoring algorithm with bonuses

10. Configuration Options (Optional Enhancement)

File: file:src/extraction/config.rs

Add optional deduplication configuration to ExtractionConfig:

enable_deduplication: bool - toggle deduplication on/off (default: true)
dedup_threshold: Option<usize> - only deduplicate strings appearing N+ times
preserve_all_occurrences: bool - whether to keep full occurrence list or just count

Update the Default implementation and validate() method accordingly.

Data Flow Diagram

sequenceDiagram
    participant Binary as Binary File
    participant Parser as Container Parser
    participant Extractor as String Extractor
    participant Dedup as Deduplicator
    participant Output as Output Formatter

    Binary->>Parser: Read binary data
    Parser->>Parser: Detect format (ELF/PE/Mach-O)
    Parser->>Parser: Extract sections, imports, exports
    Parser->>Extractor: ContainerInfo with metadata
    
    Extractor->>Extractor: Extract from .rodata/.rdata
    Extractor->>Extractor: Extract from resources
    Extractor->>Extractor: Extract from imports/exports
    Extractor->>Dedup: Vec<FoundString> (with duplicates)
    
    Dedup->>Dedup: Group by (text, encoding)
    Dedup->>Dedup: Create StringOccurrence for each
    Dedup->>Dedup: Merge tags (union)
    Dedup->>Dedup: Calculate combined scores
    Dedup->>Output: Vec<CanonicalString> (deduplicated)
    
    Output->>Output: Format as JSON/Human/YARA
    Output->>Binary: Display results

Key Implementation Details

Component	Implementation Detail
Grouping Key	`(String, Encoding)` tuple - ensures UTF-8 and UTF-16 versions are separate
Data Structure	`HashMap<(String, Encoding), Vec<FoundString>>` for O(1) grouping
Tag Merging	`HashSet<Tag>` for deduplication, then convert to sorted `Vec<Tag>`
Score Calculation	Base (max) + 5×(n-1) + section_bonus + source_bonus + confidence_boost
Sorting	Final `Vec<CanonicalString>` sorted by `combined_score` descending
Backward Compatibility	`to_found_string()` method for existing pipeline integration

Testing Strategy

Test Type	Coverage
Unit Tests	Deduplication logic, tag merging, score calculation, edge cases
Integration Tests	End-to-end with BasicExtractor, real binary fixtures
Property Tests	Verify deduplication invariants (no data loss, correct grouping)
Performance Tests	Benchmark with large string sets (10k+ strings)

Import In IDE

🤖 Prompt for AI Agents

## Observations

The Stringy codebase has a well-structured extraction pipeline with `BasicExtractor` returning `Vec<FoundString>`. The `FoundString` structure contains all necessary metadata (text, encoding, offset, rva, section, tags, score, source, confidence). The architecture document already plans for `file:src/extraction/dedup.rs` as part of the extraction module. Standard Rust collections (`HashMap`, `HashSet`) are already used throughout the codebase. The output module is currently a placeholder, providing flexibility for deduplicated output formatting.

## Approach

Implement string deduplication as a post-extraction processing step that groups `FoundString` entries by `(text, encoding)` keys, preserving all occurrence metadata in a new `CanonicalString` structure. The deduplication module will merge tags using set union semantics, calculate combined scores with occurrence-based bonuses, and maintain complete traceability. This fits naturally between the extraction and classification phases in the existing pipeline: `String Extraction → Deduplication → Classification → Ranking → Output`.

## Implementation Steps

### 1. Create Deduplication Module Structure

**File:** `file:src/extraction/dedup.rs`

Define the core data structures for canonical strings and occurrences:

- Create `CanonicalString` struct with fields:
  - `text: String` - the deduplicated string content
  - `encoding: Encoding` - encoding type from `file:src/types.rs`
  - `occurrences: Vec<StringOccurrence>` - all locations where string appears
  - `merged_tags: Vec<Tag>` - union of tags from all occurrences
  - `combined_score: i32` - calculated score with bonuses

- Create `StringOccurrence` struct with fields:
  - `offset: u64` - file offset
  - `rva: Option<u64>` - relative virtual address
  - `section: Option<String>` - section name
  - `source: StringSource` - extraction source type
  - `original_tags: Vec<Tag>` - tags from this specific occurrence
  - `original_score: i32` - score from this specific occurrence
  - `confidence: f32` - confidence score from noise filtering

- Implement `Serialize` and `Deserialize` traits for both structs using `serde` (already a dependency in `file:Cargo.toml`)

### 2. Implement Core Deduplication Algorithm

**Function:** `pub fn deduplicate(strings: Vec<FoundString>) -> Vec<CanonicalString>`

Algorithm steps:

- Create `HashMap<(String, Encoding), Vec<FoundString>>` to group strings by key
- Iterate through input `Vec<FoundString>` and populate the HashMap
- For each group in the HashMap:
  - Create `StringOccurrence` entries from each `FoundString` in the group
  - Merge tags using `HashSet` to ensure uniqueness, then convert to `Vec<Tag>`
  - Calculate combined score using the scoring strategy (see step 3)
  - Construct `CanonicalString` with all metadata
- Sort resulting `Vec<CanonicalString>` by `combined_score` in descending order
- Return the sorted vector

### 3. Implement Scoring Strategy

**Function:** `fn calculate_combined_score(occurrences: &[StringOccurrence]) -> i32`

Scoring components:

- **Base score:** Find maximum `original_score` across all occurrences
- **Occurrence bonus:** Add `5 * (occurrences.len() - 1)` points for multiple occurrences
- **Cross-section bonus:** Add `10` points if string appears in sections with different names (use `HashSet<Option<String>>` to count unique sections)
- **Multi-source bonus:** Add `15` points if string appears from different `StringSource` variants (use `HashSet<StringSource>` to count unique sources)
- **Confidence boost:** Add `(max_confidence * 10.0) as i32` where `max_confidence` is the highest confidence value across occurrences

Return the sum of all components.

### 4. Implement Tag Merging Logic

**Function:** `fn merge_tags(occurrences: &[StringOccurrence]) -> Vec<Tag>`

Implementation:

- Create `HashSet<Tag>` to collect unique tags
- Iterate through all occurrences and extend the HashSet with `original_tags`
- Convert HashSet to `Vec<Tag>` and sort for consistent output
- Return the merged tag vector

Note: The `Tag` enum in `file:src/types.rs` already implements `PartialEq` and `Eq`, making it suitable for HashSet usage.

### 5. Add Conversion Utilities

**Function:** `fn found_string_to_occurrence(fs: FoundString) -> StringOccurrence`

Convert `FoundString` to `StringOccurrence` by mapping fields:
- Extract offset, rva, section, source, confidence directly
- Move tags to `original_tags`
- Move score to `original_score`

**Function:** `impl CanonicalString { pub fn to_found_string(&self) -> FoundString }`

Create a representative `FoundString` from `CanonicalString` for backward compatibility:
- Use the first occurrence's offset, rva, section, and source
- Use merged_tags and combined_score
- Use the highest confidence from all occurrences

### 6. Integrate with Extraction Module

**File:** `file:src/extraction/mod.rs`

- Add `pub mod dedup;` declaration in the module list (around line 109-130)
- Re-export deduplication types: `pub use dedup::{CanonicalString, StringOccurrence, deduplicate};`
- Update module documentation to mention deduplication capabilities

**File:** `file:src/lib.rs`

- Add `CanonicalString` and `StringOccurrence` to the re-exports (around line 83-85)
- Update the module documentation to reflect deduplication support

### 7. Implement Comprehensive Unit Tests

**File:** `file:src/extraction/dedup.rs` (test module)

Test cases to implement:

- **Basic deduplication:** Create 3 identical strings with different offsets, verify single `CanonicalString` with 3 occurrences
- **Encoding separation:** Create identical text with different encodings (Utf8 vs Utf16Le), verify separate canonical entries
- **Metadata preservation:** Verify all offsets, sections, and sources are preserved in occurrences
- **Tag merging:** Create duplicates with different tags, verify union of tags in merged_tags
- **Score calculation:** Test base score selection, occurrence bonus, cross-section bonus, multi-source bonus
- **Empty input:** Verify empty vector returns empty result
- **Single occurrence:** Verify single string creates canonical entry with one occurrence
- **Sorting:** Verify output is sorted by combined_score descending
- **Edge cases:** Test with empty strings, very long strings, special characters

### 8. Create Integration Tests

**File:** `file:tests/test_deduplication.rs` (new file)

Integration test scenarios:

- Create a mock binary with duplicate strings in multiple sections
- Use `BasicExtractor` to extract strings
- Apply `deduplicate()` function
- Verify:
  - Duplicate count reduction
  - Metadata preservation from all occurrences
  - Score bonuses applied correctly
  - Tags merged properly
- Test with real fixture binaries from `file:tests/integration_extraction.rs` patterns

### 9. Update Architecture Documentation

**File:** `file:docs/src/architecture.md`

Updates needed:

- In the "High-Level Architecture" section (line 8), update the pipeline to show deduplication explicitly:
  ```
  Binary File → Format Detection → Container Parsing → String Extraction → Deduplication → Classification → Ranking → Output
  ```

- In the "Extraction Module" section (line 43-51), update status from "Framework Ready" to include deduplication implementation details

- In the "Data Flow" section (line 128-130), expand the deduplication pseudocode with actual implementation details

- Add a new subsection "Deduplication System" explaining:
  - Grouping strategy by (text, encoding)
  - Occurrence preservation approach
  - Tag merging semantics
  - Combined scoring algorithm with bonuses

### 10. Configuration Options (Optional Enhancement)

**File:** `file:src/extraction/config.rs`

Add optional deduplication configuration to `ExtractionConfig`:

- `enable_deduplication: bool` - toggle deduplication on/off (default: true)
- `dedup_threshold: Option<usize>` - only deduplicate strings appearing N+ times
- `preserve_all_occurrences: bool` - whether to keep full occurrence list or just count

Update the `Default` implementation and `validate()` method accordingly.

## Data Flow Diagram

```mermaid
sequenceDiagram
    participant Binary as Binary File
    participant Parser as Container Parser
    participant Extractor as String Extractor
    participant Dedup as Deduplicator
    participant Output as Output Formatter

    Binary->>Parser: Read binary data
    Parser->>Parser: Detect format (ELF/PE/Mach-O)
    Parser->>Parser: Extract sections, imports, exports
    Parser->>Extractor: ContainerInfo with metadata
    
    Extractor->>Extractor: Extract from .rodata/.rdata
    Extractor->>Extractor: Extract from resources
    Extractor->>Extractor: Extract from imports/exports
    Extractor->>Dedup: Vec<FoundString> (with duplicates)
    
    Dedup->>Dedup: Group by (text, encoding)
    Dedup->>Dedup: Create StringOccurrence for each
    Dedup->>Dedup: Merge tags (union)
    Dedup->>Dedup: Calculate combined scores
    Dedup->>Output: Vec<CanonicalString> (deduplicated)
    
    Output->>Output: Format as JSON/Human/YARA
    Output->>Binary: Display results
```

## Key Implementation Details

| Component | Implementation Detail |
|-----------|----------------------|
| **Grouping Key** | `(String, Encoding)` tuple - ensures UTF-8 and UTF-16 versions are separate |
| **Data Structure** | `HashMap<(String, Encoding), Vec<FoundString>>` for O(1) grouping |
| **Tag Merging** | `HashSet<Tag>` for deduplication, then convert to sorted `Vec<Tag>` |
| **Score Calculation** | Base (max) + 5×(n-1) + section_bonus + source_bonus + confidence_boost |
| **Sorting** | Final `Vec<CanonicalString>` sorted by `combined_score` descending |
| **Backward Compatibility** | `to_found_string()` method for existing pipeline integration |

## Testing Strategy

| Test Type | Coverage |
|-----------|----------|
| **Unit Tests** | Deduplication logic, tag merging, score calculation, edge cases |
| **Integration Tests** | End-to-end with BasicExtractor, real binary fixtures |
| **Property Tests** | Verify deduplication invariants (no data loss, correct grouping) |
| **Performance Tests** | Benchmark with large string sets (10k+ strings) |

Execution Information

Branch: main
Commit: 1c2a33b

💡 Tips

Supported Commands (Inside Comments)

Use @traycerai generate to iterate on the previous version of the implementation plan.

Supported Commands (Inside Description)

Add @traycerai ignore anywhere in the ticket description to prevent this ticket from being processed.
Add @traycerai branch:<branch-name> anywhere in the ticket description to specify the target branch for the implementation plan.

Community

Join our Discord Community to get help, request features, and share feedback.
Follow us on X/Twitter for updates and announcements.

Implement String Deduplication with Metadata Preservation #13

Description

Problem Statement

Goals

Proposed Solution

Implementation Plan

1. Canonical String Structure

2. Deduplication Algorithm

3. Scoring Strategy

4. Integration Points

Output Format Considerations

Acceptance Criteria

Technical Considerations

Edge Cases

Performance

Configuration

Related Files

Task Context

Activity

unclesp1d3r commented on Sep 30, 2025

coderabbitai commented on Sep 30, 2025

Changes Made

📝 Improved Title

📄 Enhanced Issue Body

🏷️ Labels & Metadata

unclesp1d3r commented on Nov 11, 2025

coderabbitai commented on Nov 11, 2025

✅ Keep as Single Issue

🔧 Optional Split (if needed later)

unclesp1d3r commented on Nov 13, 2025

coderabbitai commented on Nov 13, 2025

Current State Summary

✅ Title Enhanced

✅ Comprehensive Body Content

✅ Labels & Metadata Properly Assigned

unclesp1d3r commented on Jan 4, 2026

traycerai commented on Jan 4, 2026

Observations

Approach

Implementation Steps

1. Create Deduplication Module Structure

2. Implement Core Deduplication Algorithm

3. Implement Scoring Strategy

4. Implement Tag Merging Logic

5. Add Conversion Utilities

6. Integrate with Extraction Module

7. Implement Comprehensive Unit Tests

8. Create Integration Tests

9. Update Architecture Documentation

10. Configuration Options (Optional Enhancement)

Data Flow Diagram

Key Implementation Details

Testing Strategy

Import In IDE

traycerai commented on Jan 4, 2026

Plan

Observations

Approach

Implementation Steps

1. Create Deduplication Module Structure

2. Implement Core Deduplication Algorithm

3. Implement Scoring Strategy

4. Implement Tag Merging Logic

5. Add Conversion Utilities

6. Integrate with Extraction Module

7. Implement Comprehensive Unit Tests

8. Create Integration Tests

9. Update Architecture Documentation

10. Configuration Options (Optional Enhancement)

Data Flow Diagram

Key Implementation Details

Testing Strategy

Import In IDE

Supported Commands (Inside Comments)

Supported Commands (Inside Description)

Community

Metadata

Metadata

Assignees

Labels