Scoring Methodology¶

Overview¶

Seven categories, 25 points per prompt (100 total). Passing is 60% (15/25 per prompt).

Security • Performance • Maintainability • Syntax • Structure • Execution • Quality
Detailed feedback explains deductions and partial credit.

Understanding Your Results¶

Results File Structure¶

See User Guide – Running Your First Benchmark for the canonical results directory layout and file descriptions.

Enhanced Feedback Format¶

Traditional: ✅ Error handling (5/5)
Enhanced: ✅ Code Structure (3.0/3.0): ✓yaml_import, json_import, logging
Partial credit: ⚠️ Performance (1.9/2.0): ✓no_nested_loops ✗string_concat_in_loops

Grade Scale¶

Percentage	Letter Grade	Description
90-100%	A	Production Ready - Code meets industry standards with comprehensive quality
80-89%	B	Good - High quality code with minor improvements needed
70-79%	C	Satisfactory - Functional code with some quality issues to address
60-69%	D	Minimal - Meets basic requirements, significant improvements needed
0-59%	F	Fail - Major issues, security risks, or non-functional code

Scoring Breakdown¶

Prompt 1: Code Refactoring & Analysis (25 points)¶

Tests ability to refactor and improve existing Python code while maintaining functionality.

1. Syntax Validation (5 points)¶

Valid Python Syntax (5 points): Code parses without syntax errors
Import Organization: Proper import structure and organization

2. Code Structure (3 points)¶

Required Imports (0.75 pts): yaml, json imports present
Error Handling (0.75 pts): try-except blocks implemented
Logging Integration (0.75 pts): Proper logging setup
Type Hints (0.75 pts): Function annotations added

3. Execution Success (6 points)¶

Runtime Success (4 pts): Script executes without crashing
Correct Output (2 pts): Produces expected USA users filtering

4. Code Quality (3 points)¶

No Global Variables (0.75 pts): Proper scope management
Modern Path Handling (0.75 pts): Uses pathlib instead of os.path
Main Guard (0.75 pts): Proper if __name__ == "__main__" structure
Context Managers (0.75 pts): Uses with statements for file operations

5. Security Analysis (4 points)¶

No Security Vulnerabilities (4 points): Clean code with no detected security issues
SQL injection patterns detection
Hardcoded secrets/API keys detection
Path traversal vulnerability checks
Unsafe function usage (eval(), exec(), shell=True)
Partial Credit: Graduated deductions for security issues found

6. Performance Analysis (2 points)¶

Efficient Algorithms (2 points): No detected performance issues
O(n²) algorithm detection (nested loops over same data)
Inefficient patterns (string concatenation in loops, membership testing)
Memory usage patterns (full file reads, large structures in loops)
Algorithm efficiency (multiple sorts, unnecessary conversions)
Partial Credit: Deductions based on performance issue severity

7. Maintainability Analysis (2 points)¶

Good Maintainability (2 points): Code follows maintainability best practices
Function length analysis (functions >20 lines flagged)
Code duplication detection (repeated 3+ line blocks)
Variable naming quality (meaningful names, not single letters)
Complexity indicators (nested conditions, boolean complexity)
Partial Credit: Deductions for maintainability issues

Prompt 2: YAML/JSON Correction (25 points)¶

Tests ability to fix malformed configuration files and ensure cross-format consistency.

1. Syntax Validation (4 points)¶

YAML Parsing (2 points): File loads with yaml.safe_load without errors
JSON Parsing (2 points): File loads with json.load without errors

2. Code Structure (6 points)¶

Required Keys (3 points): All top-level sections preserved
Nested Shapes (2 points): Dictionary and list structures maintain correct types
Arrays vs Scalars (1 point): api_keys remains list, scalars remain scalars

3. Execution Success (8 points)¶

Deep Equivalence (6 points): Perfect cross-format equivalence after type normalization
Partial Matches (2 points): Per-key equivalent values between YAML and JSON

4. Code Quality (6 points)¶

YAML Indentation (2 points): Consistent 2-space indentation, no tabs
JSON Literals (2 points): Proper boolean/integer types, not strings
Formatting Style (1 point): Clean, readable format structure
No Duplication (1 point): Efficient data representation

5. Security Analysis (1 point)¶

YAML Safety (1 point): No dangerous YAML constructs (!!python/, anchors, references)

6-7. Performance & Maintainability (N/A)¶

Not applicable for static configuration files

Prompt 3: Data Transformation (25 points)¶

Tests ability to implement complex data transformation logic with business rules.

1. Syntax Validation (3 points)¶

File Compilation (2 points): Python file imports and compiles without errors
Function Exists (1 point): transform_and_enrich_users function is present and loadable

2. Code Structure (3 points)¶

Correct Signature (2 points): Exact function signature transform_and_enrich_users(user_list)
Basic Organization (1 point): Has return statement and iteration logic

3. Execution Success (12 points) - Major Focus¶

Function Runs (2 points): Executes without crashing on test data
ID Standardization (2 points): All user IDs converted to integer type
Email Provider Extraction (2 points): Email domains correctly extracted and added
Age Normalization (2 points): String ages converted to integer type
Business Rules Validation (3 points): Account tiers calculated according to documented rules
Graceful Error Handling (1 point): Handles malformed records appropriately

4. Code Quality (3 points)¶

Error Handling (1 point): Has try/except blocks for transformations
Type Conversions (1 point): Uses explicit type conversion functions
Readable Loops (1 point): Clear, readable iteration patterns

5. Security Analysis (1 point)¶

Safe Constructs (1 point): No dangerous constructs (eval, exec, file operations)

6. Performance Analysis (1 point)¶

Single Pass (1 point): Efficient single-pass processing without nested loops

7. Maintainability Analysis (2 points)¶

Code Organization (2 points): Function length, complexity, and naming quality assessment

Prompt 4: API Integration & Robustness (25 points)¶

Tests ability to implement secure and robust API integration with proper error handling.

1. Syntax Validation (2 points)¶

File Compilation (1 point): Python file compiles and imports successfully
Function Exists (1 point): sync_users_to_crm function is present and loadable

2. Code Structure (3 points)¶

Correct Signature (1 point): Exact signature sync_users_to_crm(user_data, api_token)
Request Structure (2 points): Proper HTTP POST formation with JSON payload and headers

3. Execution Success (7 points) - Behavioral Testing¶

Success Handling (2 points): Returns job_id correctly on 200 response
400 Bad Request (1 point): Handles 400 status appropriately without crashing
401 Unauthorized (1 point): Handles 401 status appropriately without crashing
503 Service Unavailable (1 point): Handles 503 status appropriately without crashing
Connection Errors (1 point): Handles network connection failures gracefully
JSON Parsing (1 point): Correctly extracts data from API response JSON

4. Code Quality (3 points)¶

Informative Errors (2 points): Clear error messages for different failure scenarios
Documentation (1 point): Function docstrings or type hints present

5. Security Analysis (7 points) - Highest Priority¶

Bearer Authentication (3 points): Correct Bearer token in Authorization header
No Token Leakage (2 points): API token not exposed in URL or request body
Explicit Timeouts (2 points): Request timeout specified (prevents hanging)

6. Performance Analysis (2 points)¶

Retry Resilience (2 points): Retry logic, backoff patterns, or session usage for network reliability

7. Maintainability Analysis (1 point)¶

Code Organization (1 point): Clear structure with meaningful variable names

Common Patterns and Issues¶

Security Red Flags¶

Use of eval() or exec()
Hardcoded API keys or passwords
SQL query string concatenation
Missing input validation
Path traversal vulnerabilities

Performance Anti-patterns¶

Nested loops over same data (O(n²) complexity)
String concatenation in loops
Loading entire files into memory unnecessarily
Multiple sorting operations on same data
Inefficient membership testing (list vs set)

Maintainability Concerns¶

Functions longer than 20 lines
Repeated code blocks (3+ lines)
Single-letter variable names
Deeply nested conditions (>3 levels)
Missing error handling

Bonus Points and Advanced Features¶

Models can earn bonus recognition for:

Exceptional error handling: Beyond required try-catch blocks
Clean, documented code: Comprehensive comments and docstrings
Performance optimizations: Efficient algorithms and data structures
Security best practices: Proactive security measures
Advanced Python features: Proper use of modern Python patterns

Implementation Status¶

✅ Prompt 1: Complete 7-category scoring system (5/3/6/3/4/2/2 = 25pts)
✅ Prompt 2: Complete 7-category scoring system (4/6/8/6/1/0/0 = 25pts)
✅ Prompt 3: Complete 7-category scoring system (3/3/12/3/1/1/2 = 25pts)
✅ Prompt 4: Complete 7-category scoring system (2/3/7/3/7/2/1 = 25pts)
📊 Results: Enhanced feedback across all prompts with Security, Performance, and Maintainability analysis

Implicit & Advanced Scoring Signals¶

While prompts state the core functional goals, several evaluated dimensions are intentionally only partially explicit to preserve differentiation between baseline and advanced model capabilities. This section documents them so results feel transparent without turning them into rote checklist items.

Core Expectations (Should Not Be Surprises)¶

These are security / correctness norms that affect scoring even if not always spelled out verbatim in each prompt:

Explicit network timeouts for outbound HTTP calls (Prompt 4)
No secret / token leakage outside Authorization headers (Prompt 4)
Avoid dangerous constructs (eval/exec, hardcoded credentials, unsafe YAML loaders)
Graceful partial failure handling (skip / continue rather than crash on a malformed record)
Safe file / path handling and context managers where file I/O appears (Prompt 1)

Advanced / Bonus Differentiators (Intentionally Implicit)¶

Higher‑capability models tend to infer these; they yield extra points but are not mandatory for baseline correctness:

Retry / backoff or session reuse for transient API failures (Prompt 4 resilience)
Efficient single‑pass transformations and avoiding avoidable O(n²) patterns (Prompt 3, general)
Clear modular decomposition, meaningful naming, docstrings, and type hints beyond minimal function signature
Structured, differentiated error messaging tied to specific status codes (Prompt 4)
Use of context managers, pathlib, and modern Python idioms (Prompt 1 spillover)

Flexible Acceptances¶

Scoring allows more than one “correct” representation:

Prompt 4 success return: Either the raw job_id string or a structure containing it is accepted
Partial credit on HTTP request assembly (POST + headers + JSON) even if one element is missing
Partial credit on JSON parsing if structure is partly correct

Partial Credit Patterns¶

Many categories award proportional points instead of binary pass/fail. Typical examples:

Request structure (Prompt 4): Independent credit for method, payload shape, headers
Performance & maintainability: Deductions accumulate per detected issue rather than zeroing the category

Why Not Fully Enumerate Everything in Prompts?¶

Keeping some advanced signals implicit preserves stratification—stronger models surface better engineering patterns without being spoon‑fed. Documenting them here balances fairness with challenge: authors understand why points moved without converting advanced behaviors into mandatory boilerplate.

How To Use This Section¶

If you lose points you didn’t anticipate, map the feedback line to a bucket above (Core vs Advanced). Treat Core misses as required fixes; treat Advanced misses as opportunities to differentiate.

Next Steps¶

After reviewing your scores:

Identify weak areas: Focus on categories with lowest scores
Review specific feedback: Each failure includes detailed rationale
Implement fixes: Address security, performance, and maintainability issues
Re-run benchmark: Track improvement over iterations
Compare models: Use comparison charts to evaluate different approaches

For model development guidance, see the Developer Guide.