A mixed-method in-depth study of test-specific refactorings: dataset
Notice bibliographique
Résumé
This submission is the replication package and dataset of the paper "A mixed-method in-depth study of test-specific refactorings." The files are organized in folders according to the corresponding method. Method 1 (Mining datasets) has seven files, two compressed excel sheets and five archive files. Excel sheets: Victor Guerra Veloso - Review Matrix.xlsx.zstd: this spreadsheet comprises the data from the related work as it is exported by the scripts from Gerrit2CommitMapper and MethodOneRelatedFiles.tar.zstd. This is also the input file for the association rules experiment Validation Sample ItemsetsSupport AllLevels FullyMerged.xlsx.zstd: as the name suggests it's a sample of the fully merged output of the association rules experiment. Archives: Gerrit2CommitMapper.tar.zstd contains the Gerrit2CommitMapper script that we applied to Gerrit issues to extract associated commits in the projects' git repositories. MethodOneRelatedFiles.tar.zstd contains some scripts (as python notebooks) that were used to conduct the method one. related-work-datasets.tar.zstd contains all the raw datasets from related works MiningDatasetRMinerOutput.tar.zstd includes the output of RefactoringMiner for each commit in our dataset TestRefactoringExistingDataMining.tar.zstd comprises the python project we develop to assist the replication of the method 1's association rules experiment. It expects "Victor Guerra Veloso - Review Matrix.xlsx" as input and generate all the association rules for different settings, such as using different algorithms (FPGrowth, FPMax, and Apriori), hyper-parameters (minsupport ranging from 0.01 to 0.15), and transaction granularities (commit-level, file-level, and method-level) Method 2 (Monitoring) has two compressed text files, one excel sheet, and an archived version of the RefactoringMonitor tool source code. Notice that, RefactoringMonitor includes all components (WebApp, API, and worker), configuration files (docker-compose.yml), and a README.md with instruction for execution. Text files: InitialSet: list of repositories initially monitored (based on method 1) ExtendedSet: list of repositories monitored after collecting popular repositories (based on github search) Excel sheet: The excel sheet concentrates the result of the Monitoring and Survey as well as the multiple runs of the validation by the second author. Method 3 (StackOverflow) has one excel sheet and one archive file. The excel sheet contains the results of the experiment and associates tags to the collected and studied StackOverflow questions. The archive file comprise all the scripts to collect and assist the analysis of the stackoverflow questions. It also includes both input and output data files. These scripts leverage webbrowser, the Python's standard library feature to open browsers in a specific URL, to iterate over all stackoverflow questions allowing the researcher to assign tags or create new tags. Newly added files include 'Core Devs Reassessment.tar.zstd', 'MonitoringReposCached.tar.zstd', and 'MonitoringExtension.tar.zstd' which comprise the artifacts generated during the method 2 core devs reassessment, the archive of all method 2 monitored repos from which at least one refactoring type has been identified, and the the artifacts generated during the method 2 monitoring extension, respectively. List of files: [M1]Mining Datasets/Gerrit2CommitMapper.tar.zstd[M1]Mining Datasets/MethodOneRelatedFiles.tar.zstd[M1]Mining Datasets/MiningDatasetRMinerOutput.tar.zstd[M1]Mining Datasets/TestRefactoringExistingDataMining.tar.zstd[M1]Mining Datasets/Validation Sample ItemsetsSupport AllLevels FullyMerged.xlsx.zstd[M1]Mining Datasets/Victor Guerra Veloso - Review Matrix.xlsx.zstd[M2]Monitoring/Core Devs Reassessment.tar.zstd[M2]Monitoring/MonitoringReposCached.tar.zstd[M2]Monitoring/MonitoredRepositories-ExtendedSet.txt.zstd[M2]Monitoring/MonitoredRepositories-InitialSet.txt.zstd[M2]Monitoring/MonitoringExtension.tar.zstd[M2]Monitoring/Monitoring tags.xlsx.zstd[M2]Monitoring/RefactoringMonitor.tar.zstd[M3]StackOverflow/StackOverflow tags.xlsx.zstd[M3]StackOverflow/StackOverflow.tar.zstd
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 | 0,018 | 0,076 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,013 |
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 ».