Project Templating and Onboarding With Cookiecutter: Foundations, Uses, and Guidelines
Bibliographic record
Abstract
ABSTRACT Objectives Cookiecutter is a popular, mature open‐source Python library for automating the creation of customized projects from templates. This kind of scaffolding is useful for enabling reuse, encapsulating expertise, achieve uniformity, and facilitating project creation for a range of languages and domains, including microservices, web applications, and data science. Despite their success, there is a lack of descriptions of general‐purpose project templating tools such as Cookiecutter in the literature. The objective of the paper is to provide a description of Cookiecutter that is useful for researchers and practitioners. Methods Our work is informed by our own use of Cookiecutter in the context of an industrial project. We describe Cookiecutter with the help of four different research questions. The first two relate to how Cookiecutter works (RQ1) and how it is used (RQ2). The latter two are concerned with providing guidance to users of Cookiecutter (RQ3) and identifying challenges that they may face (RQ4). Result Our answer to question RQ1 provides a succinct, high‐level description of the structure of Cookiecutter templates and Cookiecutter's execution semantics. For question RQ2, we provide an analysis of the 100 most popular Cookiecutter templates on GitHub and descriptions of three applications of Cookiecutter in different domains. For question RQ3, we identify quality attributes for Cookiecutter projects together with best practice recommendations. For question RQ4, our discussion of challenges is structured around different lifecycle activities related to the overall management of Cookiecutter templates. The potential for research results in related areas such as software product lines, feature and variability modeling, and model‐driven engineering to help address these challenges is highlighted. Conclusion The paper provides a comprehensive discussion of Cookiecutter, a successful general‐purpose project templating tool with demonstrated industrial use. The discussion covers fundamental and practical aspects of Cookiecutter and thus targets practitioners as well as researchers interested in general‐purpose templating and Cookiecutter in particular.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".