Datasets:

Felix6326727
/

beyond-accuracy-code-comprehension

license: apache-2.0
task_categories:
  - text-classification
language:
  - code
tags:
  - code-comprehension
  - llm-evaluation
  - software-metrics
  - input-output-prediction
  - code-understanding
  - benchmark
pretty_name: 'Beyond Accuracy: Code Comprehension'
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: sample_id
      dtype: string
    - name: code
      dtype: string
    - name: genome
      dtype: string
    - name: io_pairs
      dtype: string
    - name: correct_io_input
      dtype: string
    - name: correct_io_output
      dtype: string
    - name: incorrect_io_input
      dtype: string
    - name: incorrect_io_output
      dtype: string
  splits:
    - name: test
      num_examples: 12584

Beyond Accuracy: Code Comprehension Dataset

Dataset for the paper "Beyond Accuracy: Characterizing Code Comprehension Capabilities in (Large) Language Models" by Machtle, Serr, Loose & Eisenbarth (University of Luebeck).

[Paper] | [Code]

Task

Binary I/O consistency: given a Python program p, an input x, and a candidate output y, determine whether y is the correct output of running p(x).

Each sample contains a correct I/O pair (label=1) and an incorrect I/O pair (label=0). Incorrect pairs are generated via in-program shuffling — pairing an input with the output of a different input to the same program — preserving lexical and stylistic characteristics while being semantically wrong.

Quick Start

from datasets import load_dataset

ds = load_dataset("Felix6326727/beyond-accuracy-code-comprehension", split="test")

sample = ds[0]
print(sample["code"][:200])
print(f"Correct:   {sample['correct_io_input']!r} -> {sample['correct_io_output']!r}")
print(f"Incorrect: {sample['incorrect_io_input']!r} -> {sample['incorrect_io_output']!r}")
print(f"GPT-OSS 120B success: {sample['llm_gpt_oss_120b_success']}")
print(f"Cyclomatic complexity: {sample['metric_cyclomatic_complexity']}")

Dataset Summary


Columns	249
Source	Python subset of Project CodeNet
I/O generation	Type-aware fuzzing with hill-climbing type inference
Models evaluated	5 LLMs
Code metrics	224 static analysis features

Column Groups

Core Columns (10)

Column	Type	Description
`sample_id`	string	Unique identifier (`{problem_id}.{solution_id}`)
`code`	string	Python source code
`source_file`	string	Original CodeNet file path
`genome`	string	Inferred input type signature (e.g. `"is"` = integer + string)
`io_pairs`	string	JSON array of all generated `[input, output]` pairs
`num_io_pairs`	int	Number of I/O pairs generated
`correct_io_input`	string	Input for the correct I/O test case
`correct_io_output`	string	Expected output (ground truth)
`incorrect_io_input`	string	Input for the incorrect I/O test case
`incorrect_io_output`	string	Shuffled (wrong) output

LLM Evaluation Columns (15)

Per-model results from the binary I/O consistency evaluation. Each model has 3 columns:

Column pattern	Type	Description
`llm_{model}_success`	bool	`True` if the model answered all test cases correctly for this sample
`llm_{model}_num_correct`	int	Number of test cases answered correctly (out of `num_total`)
`llm_{model}_num_total`	int	Total test cases for this sample (typically 2: one correct, one incorrect)

Code Metric Columns (224)

All prefixed with metric_. Values are floats (or null if unavailable).

Size & Complexity (67 columns) — includes cyclomatic_complexity, loc, sloc, lloc, maintainability_index, code_length, Halstead metrics (h1, h2, N1, N2, vocabulary, length, volume, difficulty, effort, bugs), num_branches, num_loops, num_identifiers, num_literals, num_data_flows, parameter_count, variable_count, and more.

AST / Graph Structure (118 columns) — metric_graph_nodes_* columns counting occurrences of each AST node type: if_statement, for_statement, call, assignment, binary_operator, identifier, block, etc. Also includes graph-level metrics: num_nodes, num_edges, density, diameter, average_shortest_path_length, average_clustering.

Opcode Statistics (39 columns) — Python bytecode features: num_opcodes, sum_opcodes, avg_opcode_count, min_opcode_count, max_opcode_count, individual opcode counts (opcode_1, opcode_83, ...), opcodes_used0–opcodes_used3, and top_0_opcode_name through top_19_opcode_name.

Data Generation Pipeline

Python files (CodeNet)
        |
        v
  hill_climb.py ─── infer input types ("genome") via coverage-guided search
        |
        v
  fuzzer.py ──────── generate & shrink minimal I/O pairs
        |
        v
  export_io.py ───── create correct + incorrect (shuffled) I/O pairs
        |
        v
  This dataset

See the GitHub repository for the full pipeline code.

Citation

@article{machtle2025beyond,
  title={Beyond Accuracy: Characterizing Code Comprehension Capabilities in (Large) Language Models},
  author={Machtle, Felix and Serr, Jan-Niclas and Loose, Nils and Eisenbarth, Thomas},
  journal={arXiv preprint arXiv:2601.12951},
  year={2025}
}

License

Derived from Project CodeNet (Apache 2.0).