Sample

Dorn/python/41

Human rating uses the original dataset scale.

DatasetDorn
Human3.8
Lines8

Scores

MethodScoreScaleDataset rank
(0–100)
Posnett0.4030–1; higher is more readable
79.1Δ 3.9
Dorn0.9450–1; higher is more readable
97.8Δ 14.8
Mi0.7080–1; higher is more readable
85.8Δ 2.8
LLOC5Logical lines; fewer is more readable
78.0Δ 5.0
DeepSeek V4 Pro10.330–20; mean of three runs
42.9Δ 40.1
GPT-6.1 Sol150–20; mean of three runs
52.8Δ 30.2
BTTF0.8030–1; higher is more readable
97.8Δ 14.8
Human3.81–5; original rating
83.0

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Source regions

Feature contributions

Feature contributions

FeatureValueContribution
(log-odds)
Byte entropyHow varied the bytes in the source text are. Repeated bytes give a lower value; a more even mixture gives a higher value.4.665−6.997
Halstead volumeHow much symbolic information the code contains. It combines the total number of operators and values with the number of distinct ones.165.669−5.467
Source linesThe number of non-empty source lines.8+3.2

Intercept 8.87 + feature contributions = -0.394 (log-odds).
Score = sigmoid(-0.394) = 0.403.

Feature contributions

FeatureValueContribution
(log-odds)
Identifier layout (horizontal)How rapidly the visual pattern of identifier characters changes from left to right. It measures spatial frequency, not the meaning of the names.34+2.225
Comment layout frequencyHow frequently the layout switches between lines containing only comments or whitespace and lines containing code.9+0.718
Indentation frequencyHow rapidly indentation changes from line to line.1−0.703
Operator visual areaThe share of the code’s visual area occupied by operators such as +, =, and *.0.026+0.685
Number layout frequencyHow rapidly the number of numeric values per line changes through the code.1+0.398
Identifier layout (vertical)How rapidly the visual pattern of identifier characters changes from top to bottom.7.943−0.299
Comment layout (horizontal)How rapidly the visual pattern of comment characters changes from left to right.34−0.256

Intercept 0.07 + feature contributions = 2.838 (log-odds).
Score = sigmoid(2.838) = 0.945.

Score0.708
Score5
Run 1

The code consists solely of wildcard imports from multiple modules, which severely hampers readability. There is no visible structure or logic to evaluate—only a list of import statements. Wildcard imports make it nearly impossible to determine which names are being used or where they come from, obscuring intent and making the code ambiguous. The code is concise and flat, but this is not a positive attribute when it results in a complete lack of clarity. There is no action granularity or natural reading flow beyond the imports themselves. Overall, this snippet is unreadable in terms of maintainability and understanding, as it provides no meaningful context or explicit dependencies.

Run 2

The code is a simple Python import block, so it is readable in the sense that it is flat, concise, and each import is on its own line. However, it suffers from several clarity issues. The use of wildcard imports (`import *`) from five different modules is a major readability concern because it obscures where specific names originate, making the code's dependencies ambiguous and harder to trace. The module names are somewhat descriptive (e.g., wxSchedule, wxSchedulerConstants), but the lack of explicit imports or aliasing reduces clarity of intent. The shebang and encoding comment are standard and add minor structural clarity. Overall, while the physical layout is clean and action granularity is good, the logical organization and clarity of intent are weakened by the wildcard import style.

Run 3

The code is a short Python module consisting solely of import statements. It is readable in that it is flat, concise, and each import is a single action per line. However, the use of wildcard imports (`import *`) from five different modules severely hinders clarity of intent and code structure. It is impossible to tell which names are being used, where they originate, or if there are conflicts. This makes the module's purpose ambiguous and harms maintainability. The reading flow is natural, but the logical organization is poor because there is no grouping or explanation for the imports. Overall, the code is easy to read syntactically but difficult to understand semantically.

Run 1

Code Structure: Simple, logically organized header followed by related imports. Nesting: Completely flat, with no nested blocks. Clarity of intent: Module names suggest scheduling functionality, but wildcard imports obscure which symbols are needed and where they originate; no actual program purpose is shown. Code length: Very concise, with no unnecessary implementation. Action granularity: Each import occupies its own line. Reading flow: Straightforward and consistent, though explicit imports would make dependencies easier to understand.

Run 2

Code Structure: Simple and logically organized, with file metadata followed by a single import block. Nesting: None, making the code easy to scan. Clarity of intent: Module names suggest scheduling functionality, but wildcard imports obscure which names are needed and where they originate; no executable purpose is shown. Code length: Very concise, with no unnecessary implementation. Action granularity: Each import occupies its own line. Reading flow: Straightforward top-to-bottom flow, although explicit imports would make dependencies clearer.

Run 3

Code Structure: The shebang, encoding declaration, and imports are logically grouped. Nesting: There is no nesting, making the snippet easy to scan. Clarity of intent: Module names suggest scheduling functionality, but wildcard imports obscure which symbols are needed and where they originate. Code length: The snippet is concise, with no unnecessary implementation. Action granularity: Each import occupies its own line. Reading flow: The sequence is straightforward, although explicit imports would make dependencies clearer and reduce potential namespace conflicts.