Properties and Styles of Software Technology Tutorials
Why this work is in the frame
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Bibliographic record
Abstract
A large number of tutorials for popular software development technologies are available online, and those about the same technology vary widely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resources</i> , differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distinguishing attributes</i> , i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resource style</i> , i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it