REDUSE - Will it Work on My Machine?\\A Study on Reproducibility Smells in Ansible Scripts
Bibliographic record
Abstract
A study on reproducibility smells This study, first, identifies such programming practicesthat we refer to as reproducibility smells by conducting a comprehensive multi-vocal review. We implement a tool viz. REDUSE to identify reproducibility smells in Ansible scripts. Furthermore, we carry out an empirical study to reveal the proliferation of reproducibility smells in open-source projects and explore correlation and co-occurrence relationships among them. This tool detects programming practices, referred to as productivity smells, in Ansible scripts that can lead to reproducibility issues. What is this tool? This is a detection tool, designed to detect reproducibility smells in a pattern based mode.You can provide your ansible script in .yml format and get a .csv output containing task name, task number, smell name, reason of having the smell on the task. Content Replication Package Manual validation : This folder contains the repositories used for manual validation of the tool. In each folder, for each script we have `manual.csv`(smells detected by reviewers) file and `cleaned.csv`(smells detected by tool) file. MLR : This folder contains all the files regarding multi-vocal literature review. Reproducibility smell examples : This file contains the code for the example scenarios mentioned in the category in the paper. Qualitative analysis sheet : This file contains the github issues analysis from the projects extracted for empirical study from 'ansible galaxy'. Souce codes:**`src`**:- **`extraction`**: - **`extracted_repos`**: This folder contains text files. Each text file contains the links extracted from 9 categories of the ansible galaxy top 100 of the most-downloaded repositories after applying criteria check on the github repository. - **`script`**: - **`criteria_check.py`**: This script applies criterias on the github links. 5 stars, 50 commits, last commit not older than 1 year ago. - **`link_extractor.py`**: This script extracts github links from the given url. used for extracting the projects from the ansible galaxy. - **`script_extractor.py`**: This script gets a github link and extracts the .yml files of the repository. - **`output`**: This folder contains all the detection results on the scripts. - **`detector.py`**: - This scripts contains the main logic of the tool. - It gets the path to the ansible script file and parse it. - using parsed tasks it detects the smells for each task.- **`parser.py`**: - This scripts parses a given ansible script and returns a dictionary containing the tasks of the script.- **`smell_detection.py`**: - This scripts consists of the smell detection functions. each function is trying to detect one smell according to the rules specified and provides a message.- **`results.py`**: - This script creates 2 csv files in 2 formats as output. **`test`**:- **`testScripts`**: This folder contains the original ansible scripts from the ansible galaxy and ansible-oci-collection.- **`unit_test`**: This folder contains the unit-tests for the smell_detection functions.
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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 | 0.045 | 0.239 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".