Evaluation

Benchmarking LLMs: Beyond Pass@1 to Maintainability

Oct 12, 2026 · 3 views
Refactoringsoftware qualitycode maintainabilityAutomated TestingLLM Evaluationpass-at-k

For the past two years, the industry has obsessed over a single metric to evaluate Large Language Models (LLMs) for coding: Pass@1. While Pass@1 tells us if a model can generate a function that passes tests on the first try, it fails to answer a more critical question for enterprise development: Is the code maintainable?

As we move from experimental prototypes to production-grade systems, we need evaluation frameworks that account for code health, readability, and the ability to evolve. This post explores how we can benchmark LLMs not just on correctness, but on the quality of the code they produce.

The Limitations of Pass@1

Pass@1 is a binary metric. A generated function either passes the hidden test suite or it doesn't. This approach has several blind spots:

  • Ignores Style: An LLM might generate efficient but unreadable code using obscure one-liners or aggressive optimization techniques that humans find difficult to review.
  • Lacks Context: It doesn't measure how well the code fits into an existing codebase. Does it follow the project's naming conventions? Does it use the standard library or introduce unnecessary dependencies?
  • No Refactoring Signal: It doesn't test the model's ability to understand and modify existing code, which is a larger part of a developer's day-to-day workflow than writing from scratch.

Introducing Composite Quality Metrics

To address these gaps, we propose a composite scoring system that weights functional correctness with static analysis metrics. Here is a breakdown of the components:

1. Code Complexity (Cyclomatic Complexity)

We use tools like radiate or pylint to measure cyclomatic complexity. High complexity scores indicate code that is difficult to test and maintain. A benchmark should penalize solutions that achieve correct results via deep nesting or complex conditional logic.

2. Diff Stability and Minimal Changes

In refactoring tasks, we measure the "diff stability." A high-quality model should make minimal, surgical changes to existing code. If an LLM is asked to add a parameter to a function but instead rewrites the entire class, it should be penalized. We can quantify this using Levenshtein distance on the diff or simply counting the number of lines changed vs. lines necessary.

3. Test Coverage of the Solution

Does the generated code come with robust tests? We can evaluate the quality of the auto-generated unit tests by measuring their coverage of edge cases. An LLM that generates code but only tests the "happy path" should score lower than one that identifies potential failure modes.

Practical Example: Evaluating a Refactor

Consider a task where the prompt is: "Refactor the following function to improve readability and add error handling."


# Original Code
def process_data(data):
    if data:
        return [x for x in data if x > 0]
    return []

A model that responds with the following would score highly on Pass@1 (if tests pass) but poorly on maintainability:


# Response A: Low Quality
def process_data(d):
    if d is not None:
        try:
            return [i for i in d if i > 0]
        except Exception as e:
            return []
    return []

Response A uses obscure variable names (d, i) and catches all exceptions, which is a anti-pattern. A high-quality response would look like this:


# Response B: High Quality
def process_data(data):
    """
    Filters out non-positive values from the input data.
    
    Args:
        data: A list of numbers.
        
    Returns:
        A list of positive numbers, or an empty list if input is None.
    """
    if not data:
        return []
    
    positive_values = []
    for value in data:
        if isinstance(value, (int, float)) and value > 0:
            positive_values.append(value)
            
    return positive_values

Response B is verbose, but it is self-documenting, handles type checking explicitly, and follows PEP8 standards. A maintainability-focused benchmark would assign Response B a significantly higher score than Response A, even if both pass basic functional tests.

Implementing the Benchmark

To implement this, we can create a pipeline that hooks into the CI/CD flow of the evaluation harness:

  1. Generate Code: Prompt the LLM with the task.
  2. Static Analysis: Run flake8, bandit, and mypy on the output. Parse the output for warnings.
  3. Functional Testing: Execute the generated code against a suite of unit tests.
  4. Refactoring Validation: If the task involves modifying existing code, run a "diff check" to ensure changes are localized.
  5. Score Aggregation: Combine these signals into a weighted final score. For example: Score = 0.5 * Functional_Correctness + 0.3 * Static_Analysis_Score + 0.2 * Refactor_Efficiency

Conclusion

As LLMs become integral to our development workflows, relying solely on Pass@1 is like hiring a developer based only on their ability to pass a single coding interview, ignoring their long-term cultural fit and code quality. By introducing maintainability and refactoring metrics into our benchmarks, we can better predict how these models will perform in real-world software engineering contexts. The future of LLM evaluation is not just about what works today, but what will still work six months from now.

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