Notice bibliographique
Résumé
A study on reproducibility smells This study, first, identifies such programming practices that we refer to as reproducibility smells by conducting a comprehensive multi-vocal review and propose a first-ever validated catalog of reproducibility smells for IaC scripts. 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.We elaborate on the contents of the individual folder of this repository below. Multi-vocal literature review(mlr) This folder contains the following files. grey-literature-source.xlsx: This Excel file contains all the information about the grey literature resources. It has following sheets. Links: contains information about the type of the documents, content of the document, what IaC language or concept it is related to and the corresponding link to the document. Reviewer-1-QA: contains the marking assessment of each document from the reviewer-1 for the grey literature. Reviewer-2-QA: contains the marking assessment of each document from the reviewer-2 for the grey literature. Grey Literature Sources: contains the source numbers, best practice/ bad practice that each of the literature are mentioning and a summary. Search Queries: shows that how many documents we gain for each search query, how many filtered with the exclusion/inclusion criteria and number of final documents. smell-reference: shows a list of references that each of the smells are extracted from the them. Quality Assessment final: contains the final and merged marking assessment of each document from the both reviewers for the grey literature. smell-examples.txt: contains the example code for each of the reproducibility smell discussed in the paper. smell-descriptions.pdf: contains the detailed description for each of the reproducibility smell discussed in the paper. REDUSE - a reproducibility smells detection tool (reduse) The tool is designed to detect reproducibility smells in Ansible scripts.You can provide your ansible script in .yml format and get output of the tool as a csv file containing task name, task number, smell name, reason of having the smell on the task. Build/Configure This tool requires Python 3.8+ Install the packages from reduse\src\requirements.txt Run the tool Run the detector.py file in the reduse\src folder with the path to your desired Ansible yaml file. python detector.py '/path/to/file/folder' Or, add the path to the desired ansible files or repositories to the script and then execute /bin/bash run_detector.sh Manual validation (reduse\manual-validation) This folder contains all the scripts i.e., the subject systems and detection smells by the tool as well as by the evaluators. scripts: Selected scripts for evaluation results: Detected smells by human evaluators and the tool. Each subfolder contains two kinds of files. Files ending with manual are produced by human evaluators whereas files ending with tool are generated by the tool. Empirical study (empirical-study) Analysis scripts (analysis-scripts) This folder contains all the scripts that are used to calculate the metrics required for the empirical study, such as the smell frequency, correlation, and confusion matrix. Resources (resources) extracted-repos: this folder contains the list of the open-source repositories from nine categories hosted by the Ansible Galaxy platform. extraction-scripts: scripts to check the criteria and clone the repositories from the Ansbile Galaxy platform. repos: contains Ansible scripts for all the selected repositories used for the empirical studies. Qualitative analysis (qualitative-analysis) Ansible-galaxy-issues.pdf: contains links to the repositories, issues, and the justification for the issue related to reproducibility smell from the qualitative analysis section. Qualitative-analysis.xlsx: contains information such as repository name, issue number, root cause, smell description, and link of the issue for all the issues considered for this analysis.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,052 | 0,245 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,007 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,009 | 0,010 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,317 | 0,223 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».